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On the visual discrimination of self-similar random textures Rensink, Ronald Andy 1986

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ON THE VISUAL DISCRIMINATION OF SELF-SIMILAR RANDOM TEXTURES by RONALD ANDY RENSINK B.Sc.(Physics), The University of Waterloo, 1979 M.Sc.(Physics), The University of British Columbia, 1982 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF COMPUTER SCIENCE We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA September 1986 © Ronald Andy Rensink, 1986 In p r e s e n t i n g t h i s t h e s i s i n p a r t i a l f u l f i l m e n t of the requirements f o r an advanced degree a t the U n i v e r s i t y o f B r i t i s h Columbia, I agree t h a t the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and study. I f u r t h e r agree t h a t p e r m i s s i o n f o r e x t e n s i v e copying of t h i s t h e s i s f o r s c h o l a r l y purposes may be granted by the head o f my department or by h i s or her r e p r e s e n t a t i v e s . I t i s understood t h a t copying or p u b l i c a t i o n o f t h i s t h e s i s f o r f i n a n c i a l gain s h a l l not be allowed without my w r i t t e n p e r m i s s i o n . Department of Computer S r . f p n r p  The U n i v e r s i t y of B r i t i s h Columbia 1956 Main Mall Vancouver, Canada V6T 1Y3 Date 9 S e p t e m b e r 1 9 8 6 DE-6 (3/81) Abstract This work investigates the ability of the human visual system to discrimi-nate self-similar Gaussian random textures. The power spectra of such textures are similar to themselves when rescaled by some factor h > 1. As such, these textures provide a natural domain for testing the hypothesis that texture per-ception is based on a set of spatial-frequency channels characterized by filters of similar shape. Some general properties of self-similar random textures are developed. In particular, the relations between their covariance functions and power spectra are established, and are used to show that many self-similar random textures are stochastic fractals. These relations also lead to a simple texture-generation algorithm that allows independent and orthogonal variation of several properties of interest. Several sets of psychophysical experiments are carried out to determine the statistical properties governing the discrimination of self-similar line textures. Results show that both the similarity parameter H and the scaling ratio h influence discriminability. These two quantities, however, are insufficient to completely characterize perceived texture. The ability of the visual system to discriminate between various classes of self-similar random texture is analyzed using a simple multichannel model of texture perception. The empirical results are found to be compatible with the hypothesis that texture perception is mediated by the set of spatial-frequency channels putatively involved in form vision. ii Table of Contents Abstract ii Table of Contents iii List of Tables vi List of Figures vii List of Symbols viii Acknowledgements xi 1 Introduction 1 1.1 Overview of the Thesis 2 1.1.1 The Issues 2 1.1.2 Organization of the Work 5 1.1.3 Arrangement of the Thesis 8 2 Approaches to Texture 9 2.1 A General Characterization of Texture Perception 10 2.1.1 The Role of Texture Perception 10 2.1.2 Perceived Texture 11 2.2 Methods of Texture Analysis 12 2.2.1 Spatial Approaches 13 2.2.2 Structural Approaches 21 2.2.3 Structural-Spatial Approaches 26 2.3 Models of Texture Perception 29 2.3.1 Spatial-Feature Models 30 2.3.2 Symbolic-Structure Models 31 2.3.3 Spatial-Frequency Models 33 3 Self-Similar Random Textures 36 3.1 General Properties 37 3.1.1 Self-Similar Stochastic Fractals 37 3.1.2 Self-similar Noises 43 3.1.3 Effectively Self-Similar Textures 47 3.2 Texture Generation 48 iii 3.2.1 Basis of the Algorithm 49 3.2.2 Specification of Statistical Properties 50 4 Texture-Discrimination Experiments 53 4.1 General Format 53 4.1.1 Subjects 53 4.1.2 Stimuli and Apparatus 53 4.1.3 Presentation 55 4.2 Similarity Parameter 57 4.2.1 Procedure 57 4.2.2 Results and Discussion 58 4.3 Scaling Ratio 61 4.3.1 Procedure 63 4.3.2 Results and Discussion 65 4.4 Discriminability of Other Properties 68 4.4.1 Procedure 68 4.4.2 Results and Discussion 68 5 Discussion 74 5.1 Spatial-frequency Channels 75 5.2 Analysis 77 6 Conclusions 85 Bibliography 91 A Random fields 99 A.l Introduction 99 A.2 Mean and Covariance 101 A.3 Stationarity 103 A.4 Sample Functions 103 A.5 Fourier Analysis 104 A.6 Power Spectra 105 A. 7 Real-Valued Random Fields 107 B Fractals 109 B. l Introduction 109 B.2 Definitions 110 B.3 Deterministic fractals 112 B.3.1 Parametric Representation 115 B.3.2 Fractal Functions 118 iv B. 4 Stochastic fractals 119 B.4.1 Stationary Increments 120 B.4.2 Fractional Brownian Motion 123 B. 4.3 Fractional Gaussian Noise 125 C Technical Considerations 127 C. l Discretization of Power Spectra 127 C. l . l Discrete Fourier Transform 127 C.l.2 Self-Similarity and Discrete Images 129 C.2 Generation of Textures 130 C.2.1 Fourier Transformation 130 C.2.2 Random Number Generation 131 C.3 Monitor Calibration 133 D Values of V and Z for Threshold Textures 135 v List of Tables 4.1 discriminability of similarity parameter H 62 4.2 discriminability of scaling ratio h 66 4.3 discriminability of template function P(k) 72 5.1 values of constants for spatial-frequency channels . . . 75 5.2 predicted discriminability of scaling ratio h 79 5.3 predicted discriminability of template function P(k) . 82 5.4 comparison of ensemble values for CH,A and DH,A 84 —* —* D.l values of V and Z for h —• 1 textures 137 vi List of Figures 1.1 example of texture display 3 1.2 relation between fractals and random fields 4 2.1 spectral partitions 19 2.2 example of tree grammar analysis 25 3.1 examples of self-similar covariance functions 39 3.2 examples of self-similar power spectra 43 3.3 example of template construction 51 4.1 display format 54 4.2 presentation sequence 56 4.3 line textures above and below discrimination threshold 59 4.4 power spectra for {AH>H}, {BH,h}, and {CH,h} 64 4.5 white noise vs {BH^} 69 6.1 cross display format 89 A. l example of time series 100 B. l Construction of Koch curve 114 B.2 Construction of generalized curve 116 B. 3 Relation of descriptions of self-similar curve 117 C. l calibration curve for monitor 133 vii List of Symbols The following list contains a brief description of the symbols most commonly used in this work. As far as possible, compatibility has been maintained with the notational conventions used in other areas of study. As such, this occasionally leads to nonunique denotation. Where amibiguity arises, context should make clear the intended meaning of the symbol. a, a deterministic fractal a, a stochastic fractal a(t),a(t) fractal with intrinsic parametrization a(x), a(x) fractal with extrinsic parametrization b 0 initial position of Brownian motion path c(x) sample function for covariance of image c(x) estimator for covariance of image / ( Z J y)>fzy image, random texture f(x,y),f I V random field g grey level hd(p) generalized ball A; spatial frequency in x-direction k spatial frequency vector, k = (k, I) I spatial frequency in y-direction m(k, I), rriki modulation function n number of dimensions, number of generator segments p probability of error p(w) probability density function r geometric ratio t, t intrinsic parameter, intrinsic parameter vector u displacement in x-direction viii u displacement vector, u = (u, v) v displacement in y-direction v(t) time series v(f) stochastic process w(x) windowing function x spatial position, displacement in x-direction x spatial position vector, displacement vector, x = (x,y) y spatial position, displacement in y-direction Zjfcj zero-mean, unit-variance Gaussian random variable A arbitrary scaling factor B(i) Brownian motion B/f(r) fractional Brownian motion C contrast of image C(x) covariance function D Hausdorff-Besicovitch dimension -Djt spectral damping filter E dimension of embedding space H similarity parameter Hi(k) filter for channel i L luminance of display L(X) length of line at resolution A Mi measure on channel t P(k) spectral pattern function R(x) correlation function R(0) rotation operator Si similitude for fractal subset i S(k) power spectrum T topological dimension ix Ti size of image in dimension i Vi relative contrast of channel i Zi zero-crossing density of channel i 6 parametric distance between endpoints of increment rotation angle for generator section i cut-off frequency of spectral damping filter A lower cut-off scale for spatial self-similarity mean value of random field a standard variation of random field <p{k,l) phase of wave vector k = (k,l) ip[H, AH) psychometric function UJ lower cut-off frequency for spectral self-similarity T(x) Gamma function A,- sampling distance in dimension i A upper cut-off scale for spatial self-similarity n(e . ) orthonormal transformation for generator section i n upper cut-off frequency for spectral self-similarity X Acknowledgements First of all, I would like to thank my supervisor, Bob Woodham, for all the guidance he has given me over the past few years. In the course of writing this thesis, I have learned a great deal from him about the formulation and investigation of scientific problems. Its shortcomings aside, I would like to think that this work attempts to meet his high standards. I would also like to thank Anne Treisman of UBC Psychology for her com-ments on several aspects of the psychophysical experiments. From the all-too-few discussions we had, I learned a great deal about the design of experiments. Also thanks to Alan Mackworth of UBC Computer Science for his comments on an earlier draft of this thesis. Several graduate students have helped with various aspects of this thesis. Marcia Grabowecky of UBC Psychology provided useful feedback about psy-chophysical testing. Debbie Aks, also of UBC Psychology, helped calibrate the display monitor. Jordan Brooks, UBC Computer Science, read and commented on a few chapters of an earlier draft of this thesis. Marc Majka, UBC Computer Science, provided assistance on several technical matters. Last but certainly not least, I would like to acknowledge my great debt to Jennifer Brereton for her participation in the psychophysical experiments. She showed incredible perserverance in viewing thousands of texture displays over a period of several months. I thank her for all she has done. This work has been supported by a Research Assistantship from the Depart-ment of Computer Science, University of British Columbia, and a University of British Columbia Graduate Fellowship. xi Chapter 1 Introduction One of the fundamental tasks of vision is the detection and recognition of ob-jects in the surrounding environment. The surfaces of these objects often have characteristic textures distinguishing them from their surroundings. The ef-fectiveness of a visual system is consequently increased if it can detect such structure. Indeed, many animals appear to make some use of texture — sur-face markings often promote high visibility or provide camouflage in natural habitats [BrGr85]. Even though some form of texture perception is used by many simple organ-isms, texture perception in general has proven difficult to analyze. Attempts to place it on a firm scientific basis have had only limited success. Various charac-terizations of texture exist, but none appears capable of capturing all aspects of structural and statistical regularity. These difficulties arise in part because of the interdependence of texture perception and form perception. It is difficult to determine when the spatial structure of a surface is an intrinsic surface property, describable as texture, and when it is a collection of objects discriminable in their own right. For 1 example, a distant field of wheat is seen as a single textured surface; at closer range, the same field is distinguishable as a collection of individual plants. The transition from one description to the other has no well-defined boundary. Texture perception and form perception may therefore share a set of common mechanisms. These matters must be resolved before a computational theory of texture perception can be established. To this end, an interesting class of textures for investigation is the self-similar random textures. For these textures, any char-acteristic present at a small scale is also present at a larger scale. Consequently, their spatial structure has no well-defined partition separating object boundary and intrinsic surface structure. 1.1 Overview of the Thesis 1.1.1 The Issues This work examines the ability of the human visual system to discriminate among self-similar random textures (figure l.l). The research hypothesis is that the performance of the human visual system in this domain can show whether common mechanisms underly both form perception and texture perception. In particular, evidence is sought that texture perception is based on measurements made in parallel on the set of spatially-filtered images constituting the basis of form vision. Self-similar random textures have their origin in the work of Mandelbrot on stochastic fractals [MaNe68][Mand82]. Formally, fractals are the class of mathematical objects that have a non-integral Hausdorff-Besicovitch dimension D (see appendix B). These objects may be either deterministic or stochastic. 2 Upper texture: H Middle texture: H Lower texture: H Discriminability = percentagi = 0.5, h -»• 1 = 0.5, /i -> 1 = 0.3, h -> 1 i correct pairing Figure 1.1: example of texture display E - effectively self-similar random fields F - stochastic fractals N - self-similar noises R - random fields S - self-similar stochastic fractals M - self-similar random fields Figure 1.2: relation between fractals and random fields For surfaces in three-dimensional space, the value of D ranges between 2 and 3. When D —• 2, the surface is smooth and almost planar. When D —> 3, it appears extremely rough and jagged. The fractal dimension is therefore a measure of the roughness of a surface. For reasons of mathematical convenience, D is often expressed in terms of the similarity parameter H. For the stochastic fractals considered here, H — 3 — D, so that 0 < H < 1 (see appendix B). Many fractals are self-similar, matching themselves completely when rescaled by a scaling ratio h > 1. Self-similar stochastic fractals are widely used in computer graphics to generate highly realistic images of clouds, land-forms, and plants (e.g., [Mand75][FoFu82][Mand82]). The self-similar random textures considered in this work are instances of self-similar random fields. The general class composed of such fields includes 4 several self-similar stochastic fractals and self-similar noises (figure 1.2). Also considered here are a class of effectively self-similar random fields, for which self-similarity holds only over a limited range of scales. Taken together, the self-similar and effectively self-similar random fields form a useful domain for determining the ability of the human visual system to detect self-similarity. In particular, they allow measurement of its sensitivity to quantities such as the similarity parameter H and scaling ratio h. 1.1.2 Organization of the Work The work is divided into three distinct sections: 1. Description of the properties of self-similar random fields, both in the spatial and the frequency domains. 2. Empirical investigation of the ability of the human visual system to dis-criminate among self-similar random textures. 3. Interpretation of the empirical results in light of current theories of texture and form perception. a) Description of self-similar random fields In this work, attention is restricted to random fields that are stationary. By definition, the statistical properties of such fields remain invariant under trans-lation. A stationary random field is often represented by its covariance function C(x), which describes the statistical correlation between the values of points separated by a displacement x. Another measure is the power spectrum S (k), 5 which describes the contribution to the random field of the harmonic at spatial frequency k (see appendix A). This work develops the relations between the covariance functions and power spectra of self-similar random fields. These relations are used to show that the class of self-similar random fields contains several stochastic fractals and self-similar noises. It is also shown that H and h are insufficient to completely specify a self-similar covariance function and power spectrum. This implies that other quantities must also enter the description of a self-similar random field. The reformulation of stochastic fractals and self-similar noises provides the basis of a texture-generating algorithm. By taking the Fourier transform of a field of Gaussian random variables, it is possible to create a random field having a specific power spectrum, so that self-similar textures can be readily generated. This algorithm allows the independent and orthogonal variation of several properties of interest, including the similarity parameter H and scaling ratio h. b) Psychophysical experiments The texture-generating algorithm outlined above can produce a wide variety of self-similar random textures. Psychophysical experiments based on these textures are carried out to determine the discriminability of various statisti-cal properties. Experiments are limited here to the class of monochromatic self-similar Gaussian line textures. These are formed by sweeping a horizon-tal instance of a one-dimensional self-similar Gaussian stochastic process down through a finite vertical distance (figure 1.1). 6 Although simpler than fully two-dimensional textures, line textures are not trivial, having been used before in psychophysical research (e.g., [StJu72][RiPo74][Rich79]). Line textures have the advantage of allowing the set of possible texture elements to be reduced to a bare minimum (viz., straight-line segments together with their endpoints). More importantly here, an analytical treatment of many of their statistical properties is possible. Results obtained using these textures can form a basis for the treatment of the more general case. The texture-discrimination experiments involve a display composed of three line textures (figure 1.1). Two adjacent textures are from the same random field, while the third texture is an instance of a second field. Discriminability between the two fields is given by the percentage of correct pairings made over a series of presentations. Results show that no abrupt change in discriminability occurs between self-similar fractals, self-similar noises, and effectively self-similar textures. They also show that H and h are insufficient to completely characterize the perception of all self-similar random textures. c) Analysis of empirical results The results of the texture-discrimination experiments are analyzed using a sim-ple multiresolution model of texture perception. This model assumes that tex-ture perception is based on measurements made in parallel on a set of filtered images of various spatial resolutions. The empirical results are consistent with the hypothesis that texture discrimination is based on measurements such as the relative contrast or the density of zero-crossings in each of these images. 7 The zero-crossings present at each level of resolution are the basic elements of visual perception in many theories of form vision (e.g., [Marr82]). As such, the results of the texture-discrimination experiments are consistent with the conjecture that texture perception and form perception share a set of common mechanisms. 1.1.3 Arrangement of the Thesis A general framework for discussing the basic issues discussing texture perception is presented in chapter 2. It introduces basic concepts and definitions, briefly surveys the more popular methods of texture analysis, and examines several current models of texture perception. Chapter 3 develops the relation between the covariance functions and power spectra of self-similar random fields. The texture-discrimination experiments are presented in chapter 4. Chapter 5 discusses the results using a multiresolution model of texture discrimination, and examines their significance for a general computational theory of texture perception. Chapter 6 summarizes the general conclusions reached, and sug-gests some possible directions for future work. Appendix A is a short review of the basic concepts used in the analysis of time series and random fields. Appendix B introduces several of the main ideas of fractal geometry, emphasizing those aspects relevant to this work. Appendix C examines the effects a discrete spatial image and power spectrum have on per-ceived texture, describes the generation of the textures, and briefly describes the calibration of the monitor used to display the textures. Appendix D is a ta-ble containing the relative contrasts and zero-crossing densities of the reference textures used in the analysis of the texture-discrimination experiments. 8 Chapter 2 Approaches to Texture Over the past few decades, rigorous bases have been established for several mod-ules of low-level vision (e.g., shape-from-shading [Wood81], stereopsis [Grim81], and surface-boundary-from-velocity [Hild84]). In contrast, there has been lit-tle apparent progress on other modules such as colour and texture perception. For texture perception, principles and techniques have remained largely ad hoc [Hara79][Jule84][GoDe85]. This may be due to the inherent complexity of the processes involved. Indeed, it has been argued that the underlying mechanisms may be so complex that no concise theoretical treatment of texture perception can ever be given [Marr77]. Nevertheless, some progress has been made. Although a complete theoretical treatment is not yet possible, previous results can be described within a common framework. This is based on a general characterization of texture perception. 9 2.1 A General Characterization of Texture Perception This section discusses the general nature of texture perception, emphasizing its contribution to early vision. Attention is restricted to monochromatic broad-band images. Colour perception is considered to be a separate concern, and is not discussed here. 2.1.1 The Role of Texture Perception The functions of the early visual system include determining the location and spatial extent of objects in the surrounding environment, and providing higher-level systems with enough information to identify the objects [Marr82]. Many sources of information are available to help with these tasks, including binocular disparity, accommodation, and motion. The surface structure of the objects themselves can also be exploited for these purposes. Surface structure is the intrinsic spatial organization of a sur-face, together with its reflectance characteristics. It is largely determined by the basic physical and chemical composition of the object. Since many objects have a composition different from their surroundings, it follows that their surface structures should differ as well. These differences can help determine their lo-cation and spatial extent in an image. An important task of texture perception is therefore the segmentation of an image into distinct regions. Texture can also assist in recovering three-dimensional shape. If a surface has an isotropic structure, its orientation can be determined from texture gra-dients [Kend79] or from foreshortening effects [Breu80]. Information about surface structure can be put to further use. Since many 10 objects have a distinctive surface property, it would be advantageous for the low-level vision system to transmit a description of the surface to assist in higher-level identification or classification of the object. Another task of texture perception is therefore the extraction of information about intrinsic surface structure. Texture segmentation and shape-from-texture are not examined here. Dis-cussion is limited to uniform textures on fiat, pre-segmented regions. Issues such as projection and foreshortening are bypassed, and attention focussed on the final task mentioned: the characterization of perceived texture. 2.1.2 Perceived Texture The projection of a three-dimensional surface onto a two-dimensional image depends on the location, orientation, and illumination of the surface, as well as its intrinsic surface structure. In general, the effects of all these factors are confounded, so that surface structure cannot be completely recovered from an image. Nevertheless, a perceptual system can recover some of the surface structure. It is limited in this task by several factors, including its ability to determine three-dimensional structure from the image, and its ability to represent spatial information. Those aspects of surface structure determined from an image are referred to here as the perceived texture. The term resists an exact definition — it is used loosely here to refer to the intrinsic surface structure of a per-ceived region not containing any perceived objects. The prohibition against perceived objects is essential if texture perception is to be studied apart from the perception of objects. 11 Perceived texture can be characterized in several different ways, depending on the complexity of the image and the conditions under which it is viewed. When an image is so disorganized that objects cannot be perceived in it without a considerable effort of will, it is commonly termed a random texture. Texture can be perceived in such images under all conditions. Some images contain spatial features that can be combined into simple objects when attended to consciously [Trei85]. To avoid the effects of conscious scrutiny, texture per-ception must be limited to non-attentive viewing [Marr76]. When an image contains only a few items, these are often perceived as objects in their own right. To study texture using such images, it becomes necessary to consider texture perception as a pre-attentive process, taking place within the first few hundred milliseconds of image presentation [Jule75]. 2.2 Methods of Texture Analysis One of the central problems in texture perception is to determine the particular aspects of surface structure that are most useful for identification and classifi-cation. Although work has been done on coding principles applicable to these tasks (e.g., [Cael84]), a complete theoretical understanding of these issues has not yet been achieved. Empirical evidence is consequently of great value. In this regard, results obtained from the machine analysis of texture are of interest. Historically, several different approaches to texture analysis have been taken. This has led to a great variety of representations. Each emphasizes some partic-ular aspect of an image, such as its periodicity, structural hierarchy, or intrinsic spatial features present. All approaches, however, describe a texture by its mi-crostructure and macrostructure [Hara79][Breu80]. The microstructure is the 12 set of basic elements, or texels, that form the texture. The macrostructure is the set of spatial relations that exist between the microstructure elements. Differ-ent approaches to texture analysis are characterized by the the microstructure and the macrostructure that they use. 2.2.1 Spatial Approaches Spatial approaches treat texture as a collection of simple elements spread densely throughout a region. These elements form a continuum, parametrized by some co-ordinate system. Depending on the the continuum and the elements used, a spatial approach can be placed into one of three groups: point statistics, global transforms, or local transforms. In the first group, the continuum is taken to be a two-dimensional geometric space, and the elements are the individual points in the image. These methods describe texture by the statistics of the intensity values at these points. The second group involves global transformation of the original image (e.g., using the Fourier transform). The continuum is given by the transform space of the new representation. Each point in this space represents a specific pattern of intensity values in the original image. The third group of methods is based on local transformation. The continuum is a two-dimensional geometric space, while the texture element at each point describes the structure in the local neighbourhood. In all these approaches, the elements are parametrized by an underlying continuous space. The term 'spatial' is used here in this more general sense. 13 a) Point statistics This form of texture analysis is based on the statistics of the intensity values of individual points in an image. Images are generally assumed to be instances of ergodic random fields (see appendix A), whose spatial averages reflect ensem-ble properties. The various methods used are distinguished by the statistical properties represented. The simplest representations involve first-order statistics, which are based on the histogram of the intensity values present. To reduce the effects of unequal lighting or poor instrument calibration, the averages and standard deviations (see appendix A) of the images analyzed are often set to common values. This destroys much of the first-order information. On occasion, however, informa-tion from unequalized images is used. The earliest first-order representations (e.g., [Rose62][PrMe66][DaJo68]) made use of several properties, such as mode and skewness. However, first-order statistics generally contain little informa-tion apart from that contained the average and standard deviation [AhDa77]. In current practice, these are often the only first-order quantities measured [Hara79]. First-order statistics cannot completely describe a texture, since they have no reference to the spatial arrangement of the elements. To capture this struc-ture, higher-order statistics must be used. Second-order statistics are based on the frequency of the joint intensities of pairs of pixels separated by various displacements. Julesz [Jule62] made the conjecture that the discriminability of random-dot textures is completely determined by their second-order statistics. Over the years, this conjecture has inspired many analytic methods based on second-order statistics. 14 Measures based on such statistics were among the earliest used for texture analysis: Kaizer, in 1955, used the autocorrelation function C(u) as the basis of texture description [Hara79]. This function is the second-order moment of the joint probability density. For an image f(x,y) of dimensions Tx x Ty, it has the form ^ ( t t i v ) = / / f{x + u,y + v)f(x, y) dxdy, where u is the horizontal displacement between the pair of pixels, and v the vertical displacement. A related function is the covariance function C(u), defined by C(u,v) = TrTTfT f j (f(x + u,y + v) - fi){f{x,y) - fi) dxdy, where n is the average intensity of the image (see appendix A). The two mea-sures are related by C(xi, x2) = R{xu x2) - M 2 , showing that the second-order information they contain the same. Various properties of the covariance function are used for texture classi-fication, including spatial moments, autoregression parameters, and concav-ity/convexity of form [Laws80][ChKa81]. Measures based on the covariance function do not result in highly accurate texture analysis; other second-order quantities must be used as well [Laws80]. This agrees with the observation that for human perception, the mean, variance, and covariance function are insufficient to determine the perceived texture [PrFa78]. A more general system of second-order statistical features was proposed by Rosenfeld and Troy in 1970 [Hara79], and later developed by Haralick et al [HaSh73]. This approach is based on the grey level dependence matrix, which 15 describes the frequency of joint intensities of pixel pairs as a function of their spatial separation. This approach is a development of the Markov models first used by Julesz [Jule62], who analyzed texture using the transition probabilities between the values of neighbouring pixels. The grey level dependence matrix corresponds to the second-order joint den-sity function of a stationary random field. As such, its description can be large: for an image of size n X n pixels with m grey levels per pixel, the complete ma-trix would have a size of order n.2m2. Furthermore, few pixels are separated by displacements comparable to the size of the image. The determination of joint intensity distributions for such pixel pairs is therefore susceptible to statistical fluctuation. To overcome these drawbacks, a small set of features based on the grey level dependence matrix is used. Haralick et al [HaSh73] proposed a set of 14 measures, one of which was the covariance. To further reduce the size of the description, only a few orientations and separation distances are chosen. The re-sultant descriptions prove to be useful for texture identification, leading to over 90% classification accuracy in certain texture domains [WeDy76][CoHa80a]. Less arbitrary methods of reducing the description size have been devel-oped. An optimal set of pixel displacements can be determined by statistical tests on the matrices [ZuTe80]. These can lead to similar classification accuracy with fewer features. Absolute pixel values can also be discarded, keeping only the relative differences in pixel intensities [WeDy76]. Although generally not as powerful as the approach based on grey level dependency, the use of rela-tive intensities leads to nearly similar classification accuracies in many texture domains [WeDy76][CoHa80a]. 16 Second-order information is also contained in the fractal dimension of the image [Pent83][MeYa84]. This quantity is determined by the rate at which the increment f(x + A) — f(x) increases as a function of displacement A (see appendix B). Using only the fractal dimensions measured in the x and y direc-tions, classification accuracies of up to 85% can be achieved for several classes of natural textures [Pent83]. b) Global transforms Perhaps the simplest way to represent an image is to assign an intensity to each point. When searching for specific spatial patterns, however, it is often useful to determine a global transform. This describes an image in terms of a basis set of spatial functions. For example, the finite Fourier transform describes an image as a (possibly infinite) sum of sine and cosine functions. This transform makes clear the degree to which the image is periodic. Global transforms contain no explicit reference to spatial position — the image is described only in terms of the basis functions. If the basis set is complete, however, the transform contains all the information present in the original image [RoKa82]. Various transforms have been used for texture analysis, including Hadamard transforms, slant transforms, and Fourier transforms. Although their effective-ness for texture discrimination appears to be similar [Kirv76], only the Fourier transform is widely used. The Fourier transform f(k, I) of a continuous image f(x, y) is given by 17 where k and / are spatial frequencies in the x and y directions respectively. This function is often written as a product, viz., where m(k,l) = /)| is the amplitude of the waveform, and <f>(k,l) is its phase. This class of representations emphasizes spatial periodicity. As such, it is most useful for the analysis of periodic patterns. However, it is also useful for random patterns as well. Many ways exist to form equivalence classes of images based on their Fourier features, but only a few have been seriously investigated. Although the phase is important for images with global structure [JuBe83], the information it contains is generally of little use for classifying textures [Eklu79]. Some texture models are based on the sum of a few narrow-band sources of noise [Scha80], but only a few of these have been explored. Most approaches follow the lead of Lendaris and Stanley [LeSt70], who used the power spectrum of the image as the basis of texture analysis. These approaches are based on the partitioning of frequency space into bins of varying shape. Description of a texture is given by the summed contribu-tions of the power spectrum in each of the partitions. Three distinct types of partitioning are commonly used [CoHa80a]: annular rings, angular wedges, and parallel slits (figure 2.1). Annular rings provide a representation based on spatial frequency alone. Each ring corresponds to waveforms of arbitrary ori-entation, with frequency within some bounded range. Angular wedges allow a description of the directionality of the texture: each wedge corresponds to those waveforms oriented between two specified angles. The parallel-slit geometry is formed by a series of narrow, parallel rectangular regions. These are useful for 18 (a) annular rings (b) angular wedges (c) parallel slits Figure 2.1: spectral partitions detecting one-dimensional structure at a given orientation. Fourier-based representations have been used for accurate classification of many natural textures [LeSt70][Bajc73][WeDy76], but in general are less useful than the statistical representations [CoHa80a]. c) Local transforms This type of texture analysis is based on the local structure present at each point in the image. This is done using local transforms, which extract information from the neighbourhood surrounding each point. The form of these transforms depends on the the local structure considered relevant. The results of several different transforms can be incorporated as feature planes into a composite description [Laws80]. Each element of the microstruc-ture is then described by a vector quantity. These feature vectors can be given new bases in a generalized feature space, and be condensed down into a space of fewer dimensions. The various representations can be characterized by the local transform and the feature space used. 19 The earliest local transforms were spatial transforms, obtained by convolving a spatial filter over the image. Rosenfeld [Rose62] used the one-dimensional first-order derivative of an image as a basis for texture analysis. Classification was done via the first-order statistics of these derivatives. Linear filters emphasizing such shapes as lines, wedges, and spots have also been used [Hawk70]. More recent approaches use sets of general spatial filters. Laws [Laws80] uses a complete basis set of 3 x 3 and 5x5 masks, that describe averaging, first-differencing, and second-differencing operations. These filters are sums and differences of Gaussian functions [PiRo83]. By using texture energy mea-sures based on the first-order statistics of the resulting elements, a classification accuracy of over 90% has been obtained for many classes of natural texture. Such accuracy generally depends on an appropriate choice of resolution size for the masks [Dumo85]. Methods have been developed [Ade83] to automatically select the best filter masks. Another class of local transforms are the textural transforms introduced by Haralick [Hara75][Hara79]. The value of each element in the transformed image is a function of the grey level dependence matrix for the neighbourhood that surrounds it. Analysis is based on the first-order statistics of the elements of the transformed image. Accuracy is generally not as good as when the statistics of spatial transforms are used [Laws80]. d) General performance Methods based on spatial approach have several common strengths and weak-nesses. To begin with, they are all highly sensitive to the values of the intensities in an image — small changes to these values can lead to large changes of de-20 scription. Such variations are almost always present between different images of the same texture, owing to uneven lighting, lack of camera calibration, etc. Some robustness can be instilled by equalizing the image histograms, so that all values are equally distributed [Hara79]. Methods based on local transforms must specify in advance the size of the neighbourhoods used. This renders texture description dependent on scale. More recent approaches, such as the fractal-based descriptions of Peleg et al [PeNa84], use measurements made at multiple scales of resolution to achieve a degree of scale-independence. Another drawback is the inability of spatial approaches to capture the struc-ture of higher-order groups of texture elements. This results largely from the homogeneous treatment of texture elements they employ. In spite of these problems, spatial approaches are widely used. To begin with, they are indifferent to the pattern contained in the image — the com-putational resources required depend only on the size and the number of grey levels of the image. Furthermore, the descriptions are easily formed, so that many different measures can be created. These can then be combined into composite measures that lead to some of the highest classification accuracies yet achieved: over 80% for general classes of texture, and over 90% for more restricted domains [Hara79][Laws80]. 2.2 .2 S t r u c t u r a l A p p r o a c h e s Many images are highly regular in their spatial structure. Structural approaches take advantage of this regularity by restricting the ways in which basis functions can be combined. The resulting constraints enable compact descriptions to 2 1 be made. Such representations generally involve only a few microstructure elements, arranged in patterns generated by a set of placement rules. Equivalent images are exactly those that can be described by the same microstructure and placement rules. Although the possibility of such approaches has been discussed for many years (see, e.g., [Hawk70]), their development is a recent occurrence. Two groups of structural methods have been developed. In the first group, texture is considered to be composed of identical elements arranged in regular fashion throughout the plane. The second set makes use of syntactical techniques, representing texture as a parsing of the image. a) Regular placement These methods analyze texture by partitioning space into contiguous regions of identical spatial structure. The patterns in these regions form the microstruc-ture of the texture. They may have a complex form, often being hierarchically composed of sub-elements [MaSa82]. The macrostructure is a two-dimensional periodic lattice, whose nodes describe the locations of the texture elements. Any image with a periodic structure can be partitioned this way [CoHa80b]. Two strategies are commonly used to create such descriptions: bottom-up and top-down. In the bottom-up approach, grouping processes are used to form the basic elements, while a clustering operation uses the locations of their centers to determine the macrostructure [MaSa82]. Such techniques can correct for missing of erroneous elements in the image, but generally remain extremely sensitive to noise, blur, and geometric distortion [MaMi83]. Most of these limitations can be bypassed by using top-down techniques. 22 The periodicity of the elements is first determined. This can be done via the grey level dependence matrices [CoHa80b][ZuTe80], or by Fourier analysis [MaMi83]. Elements can then be determined via region growing from the nodes of the macrostructure lattice. Although not suitable for general use, these techniques can provide structural descriptions of many periodic and nearly periodic patterns. [CoHa80b][MaMi83]. b) Syntactic approaches This approach is an outgrowth of picture grammars [Rose71]. A set of termi-nal symbols, made up of spatially-connected pixels, specifies the microstructure elements. A set of non-terminal symbols specifies the placement rules. Each non-terminal symbol corresponds to a fixed, two-dimensional template specify-ing the relative locations of several other terminal and non-terminal symbols. Such an approach is reminiscent of the grammars used for the syntactic analysis of languages. Indeed, the structural approach has been consciously developed along such lines — analysis is based on a parsing of the visual texture. The various grammars differ in their specification of the terminal and non-terminal symbols. Shape grammars [BaBr82,ch6] use complex geometrical shapes for their terminal symbols. The placement rules are local in nature; they are represented by non-terminal markers that allow several adjacent sym-bols to be combined. A more complete separation of microstructure and macrostructure is achieved in tree grammars [FuLu78]. The placement rules have the form of two-dimensional trees; these are combined to form the macrostructure of the 23 texture (figure 2.2). Texture elements are then inserted into the resulting ar-rangement of terminal markers. To make this approach feasible, an image is first segmented into an array of rectangular windows, each of which is then analyzed. This is done to avoid the effect of large-scale warps of an ideal tex-ture. Small-scale perturbations are handled by using a stochastic grammar, in which the placement rules can be selected nondeterministically. Combining these with an error-correcting bottom-up parser allows reasonably good dis-crimination among several classes of natural textures with large-scale structure [Fu82,chl2]. More complex approaches analyze the structure of the elements themselves. This is done by using levels of different grammars, the terminal symbols of one level being the starting symbols of the next one down [Jaya79]. The resulting descriptions are hierarchical in form, the placement rules at each level describing corresponding groupings in the image. Placement rules can also be recursively applied to scaled-down versions of themselves, resulting in descriptions with an infinitely many structural levels. Such grammars can be used to describe the self-similar deterministic fractals (see appendix B), that have similar spatial features at all levels of detail. c) General performance A purely structural approach is unsuitable for domains where few constraints exist on spatial structure, for spatial regularity is lost, and the descriptions become much larger. Furthermore, such descriptions are sensitive to noise in the image, a small perturbations in the image often leading to a large change in its description. To avoid some of these drawbacks, Zucker [Zuck76] proposed 24 (VN,VT,P,S) {S,AU A2,Az,A4,N0,Ni, 1,0} N0 — l—N0 A4 -+ N0 — l — N0 0 -> • N0 — l—N0 Ni — 1 — N i A4 N0 0 , 0 N0 1 , 1 N (a) tree grammar G (b) pattern 0 0 0 0 — 1 — 1 — 0 — 0 0 0 (c) tree rep — 0 — 0 — 0 — 0 — 1 — 1 0 — 0 0 0 esentation Figure 2.2: example of tree grammar analysis 25 that there are two aspects of any natural texture: an ideal regular texture that forms its deep structure, and a spatial mapping that distorts it into the surface structure appearing in the image. This model of texture has led to several syntactic methods of texture analysis (e.g., [Fu82,chl2]). Even for highly regular structures, a large amount of syntactic ambiguity is inevitable — many possible grammars exist for any given spatial pattern [Zuck76]. Before a structural description of a texture can be given, several a priori decisions must be made about its structure. When this is feasible (e.g., for classification of biological tissues), structural approaches prove useful for texture analysis. 2.2.3 Structural-Spatial Approaches These approaches are hybrid, attempting to combine the best aspects of struc-tural and spatial methods. As for a structural approach, microstructure ele-ments are considered to be sparsely distributed throughout the image. The relations between them, however, are analyzed using spatial techniques. Local spatial structure can therefore be concisely described without imposing large constraints on the overall global structure of the texture. Structural-spatial approaches characterize texture as a sparse set of spatially-ordered, structured elements. Each element is generally represented by a feature vector, whose values are obtained from the surrounding neighbour-hood. These neighbourhoods are generally non-overlapping regions of finite extent, which may or may not form a partition of the plane. Structural-spatial approaches can be divided into two groups, depending on how they characterize the neighbourhoods. The first group considers neighbour-26 hoods as being contiguous areas of uniform grey level. The second group de-fines neighbourhoods using local extrema. All approaches can be characterized by the information extracted from each neighbourhood, and by the statistical properties of the resulting texture elements. a) Uniform areas One of the simplest ways of specifying neighbourhoods is to partition the image into a set of unidirectional grey level runs. These runs are defined as maximal collinear strings of constant grey level, oriented in some given direction [Gall75]; they are described by their run length, direction, and grey level. Description of a texture is based on the joint occurrence of grey level and run length in each direction. To reduce the size of a description, a set of five features is computed for each direction. These are similar in many ways to some of the measures developed by Haralick et al [HaSh73]. In general, run-length measures are not as useful as second-order statistics [WeDy76][CoHa80a]. Extensions of this approach to two-dimensional regions of constant grey level have been used for texture analysis [MaBr77][ToSH82]. Properties used for classification include the area, elongation, and grey level of the regions. Clas-sification accuracies of over 80% can be achieved for several classes of natural texture [ToSh82]. b) Local extrema Local extrema in an image form the basis for several structural-spatial meth-ods of texture analysis. For one-dimensional extrema, the only features are height and width; these are measured using the neighbouring extrema. For two-27 dimensional extrema, neighbourhood boundaries can be formed in a variety of ways. One possibility is to associate with each extremum a reachability set, a set of points that can be reached from it along a monotonically increasing or decreasing path. Various properties such as its size, mean, and variance could be used [Hara79]. Such an approach, however, has not yet been thoroughly investigated. One-dimensional local extrema form the basis for an extremely efficient tech-nique for texture analysis — the max-min method [MiMy77]. The image is first smoothed to eliminate small fluctuations, and the logarithm taken to render the description independent of absolute intensity. Local extrema are then de-termined, being thresholded by a value of T above/below the neighbouring pixels. Description is given by the density of extrema for various values of T. Such a method is extremely fast, and has a classification accuracy comparable to those based on grey level dependence matrices [MiMy77]. Texture analysis has also been based on the local maxima of images filtered with the Laws masks [PiRo83]. Using only the first-order statistics of these maxima, a classification accuracy can be achieved that equals that of the original texture energy measures. This shows that the local maxima alone may contain all the essential information in texture [PiRo83]. Generalized co-occurrence matrices [DaJo79] describe texture using the re-lations between the local extrema present in an image. These matrices have a form much like the grey level dependence matrices, but their features are much more general: the joint occurrence of any property of neighbouring extrema can be used. When properties of local maxima of smoothed images are used in these matrices, classification accuracies can be achieved that are higher than 28 those obtained using grey level dependency measures [DaJo79]. c) General performance Structural-spatial analyses combine some of the best aspects of spatial and structural methods. Descriptions are readily determined, and are generally ro-bust under small geometric distortions of the image. Furthermore, the analyses are also robust under monotonic changes of grey level. Methods based on areas of uniform grey level, however, are sensitive to noise in the image. Smoothing the image would help somewhat, but the application of a smoothing filter would tend to alter the distribution of grey levels in many parts of the image, especially in areas near a boundary. This type of analysis is therefore inherently sensitive to noise. Descriptions based on local structure are more suitable for texture analysis. Since the locations of extrema are invariant under monotonic transformations of grey level, descriptions tend to be robust, even under local filtering of the image. In addition, the classification accuracies of these methods are among the highest yet achieved [GoDe85]. This shows that local extrema can form the basis for robust and accurate analysis of texture. 2.3 Models of Texture Perception Texture perception has been investigated using several psychophysical tech-niques. These generally involve restricted domains of synthetic textures, which are designed to isolate the spatial structure relevant to perceived texture. Al-though limited in scope, these methods have yielded valuable information about the ability of the human visual system to perceive texture. 29 Three different approaches to studying texture perception have been devel-oped. Each is based on a somewhat different model of the process, and has its own distinctive character. Although some parts of the various models con-flict, the three approaches are largely complementary, each modelling somewhat different aspects of a highly complex process. 2.3.1 Spatial-Feature Models This approach concentrates on determining the spatial features that influence perceived texture. Texture perception is considered to be a pre-attentive pro-cess, occurring within the first few hundred milliseconds of presentation. Em-phasis is placed on determining the necessary and sufficient conditions for two adjacent texture fields to be pre-attentively discriminable. This may equiva-lently be viewed as establishing the conditions under which two textures are perceptually identical, or metameric. This approach tends to be somewhat phenomenological in nature — little emphasis is placed on determining the underlying mechanisms involved. The spatial-feature approach has its origins in the work of Julesz [Jule62] on the discrimination of random Markov textures. Based on these results, the conjecture was made that third- or higher-order statistics are irrelevant for tex-ture discrimination. For textures with elements not locally distinguishable, the Julesz conjecture still appears to hold [PrFa78][Gaga81]. Second-order mea-sures sufficient for discrimination are not known in general. The mean and variance, together with the covariance function, are not sufficient to describe texture completely [PrFa78]. All counterexamples to the original Julesz conjecture involve texture ele-30 merits that are distinguishable locally [JuGi73][CaJu78]. This observation has led to the hypothesis that perceived texture depends only on the first-order den-sities of a specific set of localized spatial features [ Jule75] [ Jule81] [Beck82]. These textons are localized geometric shapes with simple properties; they include end-points, elongated blobs, lines of various widths and lengths, and line-crossings. Texton properties include colour, binocular disparity, and orientation [Jule81]. Since only first-order densities are involved, the relative positions of textons to each other should not affect pre-attentively perceived texture. This prediction agrees with experiment [JuBe83][Jule84]. Textons have much in common with the set of pre-attentively distinguishable features found by Treisman [Trei85]. However, they are not to be identified with the elements of form vision, since they are considered to be part of a separate pre-attentive visual system [Jule84]. The texton theory, as developed by Julesz, has been largely based on the perception of simple texture elements scattered sparsely throughout an image — no algorithm need be given of how the descriptions are calculated. If the perception of more natural textures is to be understood, however, the determi-nation of this process is essential. Caelli [Cael84] has taken a few steps toward this goal, showing that textons are members of a more general class of coding units. 2.3.2 Symbolic-Structure Models A model of texture perception more concerned with underlying algorithm and mechanism is that proposed by Marr [Marr76][Marr82]. Texture perception is considered to be a non-attentive process, employing the same grouping oper-31 ations and symbolic structures as used in form vision. As such, there is no separation between segmentation and classification. Texture discrimination is only one aspect of texture perception that can be treated using this approach — texture flow and grouping can be modelled as well. As described by Marr, the basic elements of texture are exactly the basic elements of the primal sketch: blobs, endpoints, and lines. Each is represented by a token describing its size, location, contrast, orientation, etc. Various ag-gregation processes use local properties such as common orientation to create higher-level symbolic structures. This grouping can be done recursively, build-ing up highly complex elements. Texture discrimination is assumed to be based on the first-order density of the symbolic structures present locally. Such an approach can account for many of Julesz's results [Marr76]. In addition, several classes of metameric textures with different second-order statistics can be identified via the first-order statistics of virtual lines [Scha78]. These lines are purely symbolic structures, connecting pairs of dots in the primal sketch. More generally, they can connect arbitrary elements of the primal sketch [Marr82]. Virtual lines can also be used to show how local processes can cause the Moire effect or texture flow seen in Glass patterns [Stev78]. The symbolic-structure approach has proven difficult to develop, largely ow-ing to its inherent complexity. Even the tokens are difficult to ascertain [Rile8l]. The operation of the grouping processes must also be reconciled with the in-difference of perceived texture to the relative positions of the texture elements. More recent approaches (e.g., [Zuck84]) have tended to explain many of these processes by simple spatial operations applied to simple spatial elements. 32 The symbolic-structure and spatial-feature approaches are similar in several ways. Both make use of a basic set of simple elements that, apart from the line-crossings included by Julesz, are of much the same form. The properties that these elements have are also similar. For simple textures, then, these two models generally make similar predictions about which textures are discriminable. Ontologically, however, these basic elements are distinct: primal-sketch el-ements are the basis of (attentive) form vision, while textons are part of a completely separate pre-attentive system. This is reflected in the distinction drawn between pre-attentive and non-attentive perception. The elements of the symbolic-structure approach, being part of a more powerful form vision system, can be grouped into higher-level features that may enter the descrip-tion of a texture. The spatial-feature approach, on the other hand, explicitly rejects constructive processes as having a role in texture perception [Jule84], In this view, texture perception involves only detection processes based on a set of simple spatial features. 2.3.3 Spatial-Frequency Models This approach models the attentive perception of random textures using a set of parallel spatial-frequency channels. Each channel describes the convolution of the original image with a specific filter. By studying the apparent similarity of various random textures, some insight can be gained into the structure of these filters, since similar textures should be exactly those that have similar properties in each channel. Spatial-frequency channels have their origin in the work of Campbell and Robson [CaRo68]. From studies on threshold spatial vision, Wilson et al 33 [WiBe79] [WiGe84] determined the shape of the channel filters as being the siims and differences of several Gaussian functions. Since the Fourier transform of a Gaussian function is another Gaussian function, the general form of these filters are similar in both the spatial and the frequency domains. A set of 4 - 6 filters is postulated, the individual filters being nearly identical in shape, and differing in size from each other by a factor of approximately two. Richards and Polit [RiPo74] were the first to explain perceived texture using spatial-frequency channels. They established that only four different combina-tions of spatial frequencies are needed to serve as the basis functions of a per-ceptual space for line textures. Any line texture can therefore be perceptually matched by an appropriate linear combination of these functions; this suggests that there exist four physiological spatial filters mediating texture perception. Interestingly, the shapes of these filters correspond closely to those later determined by Wilson et al [WiBe79] from work on threshold vision. These filters have also been shown to form a possible basis for the grouping of texture into classes of apparent similarity [HaGe78][HaGe81], The spatial-frequency approach has several inherent advantages and disad-vantages for texture representation. On the positive side, the descriptions are reliable [MaMo84]: they are easy to compute, are invariant under translation and rotation, vary continuously with change in the image, and capture infor-mation at several levels of detail. In addition, a metric can be established to determine the distance separating two dissimilar textures. On the other hand, this approach leaves unspecified the characteristic features (if any) being mea-sured in each channel. This makes it difficult to generalize from line textures to the fully two-dimensional case. Furthermore, many shapes are possible for 34 the filters characterizing the channels, so that assumptions must be made about their form. Unless firm links can be established between texture perception and other aspects of vision, any spatial-frequency model must contain a large degree of arbitrariness. This work examines one possible link, investigating whether texture per-ception is based on measures such as the relative contrasts and zero-crossing densities in each of the filtered images. In this regard, the near-identity of the postulated filters under rescaling suggests that it is interesting to examine the discrimination of self-similar random textures. Assuming spatial-frequency fil-ters of the form proposed by Wilson and Gelb [WiGe84], analysis shows that the texture-discrimination results are consistent with the multiple-channel model. The use of spatial-frequency channels to model texture perception is com-patible with the assumptions of the other two approaches. If the frequency bandwidth of a filter is sufficiently large, the corresponding convolution mask can have arbitrarily fine resolution in the spatial domain. As such, the features present in these multiresolution images may be precursors for the basic elements of the spatial-feature and symbolic-structure approaches. Multiresolution representations have been successfully used for modelling several other aspects of vision [Grim81][Terz82][Burt84]. Whether such an ap-proach also provides a good model for texture perception remains to be seen. 35 Chapter 3 Self-Similar Random Textures Increased attention has recently been given to the modelling of random textures by self-similar stochastic fractals (e.g., [Pent83][MeYa84]). Such objects, intro-duced by Mandelbrot [MaNe68] [Mand82], have a self-similar structure — any characteristic present at a small scale is also present at a larger scale (see ap-pendix B). Their spatial structure is therefore complex, with no well-defined par-tition existing between object boundary and intrinsic surface structure. Many random textures can be accurately described as fractals, and calculation of their fractal dimension (see appendix B) has led to classification accuracies as high as 85% [Pent83][PeNa84]. Furthermore, the fractal dimension of a surface appears to correlate closely with its perceived roughness [Pent84]. In order to investigate the ability of the human visual system to discriminate among random textures with different fractal properties, it is useful to relate these properties to more conventional descriptions of texture. This chapter shows how this can be done. It also shows how textures with fractal properties can be viewed as special cases of a more general class of self-similar random textures. These are made up of instances of ra-dimensional random fields with 36 power spectra S(k) such that for some h,H,i £ [S(hk) - 7«(o)l = h-n-2H[s(k) - ^(0)], where A; is the spatial frequency, and 6(0) is the Dirac delta function. This work examines the factors affecting the discriminability of self-similar random textures. A straightforward algorithm is developed to generate such textures for use in the psychophysical experiments described in chapter 4. 3.1 General Properties All statistical properties of a stationary Gaussian random field are completely governed by its mean n and covariance function C(x), or equivalently, by its mean (i and power spectrum S(k). Intuitively, any self-similar structure in such a field must be reflected in some form of self-similarity in its covariance function and power spectrum. This section examines the form of the covariance function and power spec-trum for self-similar random fields. Since self-similarity is characterized here by a two-point measure, third- and higher-order statistics are not relevant. The restriction that the field be Gaussian can be therefore be relaxed. In what follows, the field f (x) is taken to be any n-dimensional stationary random field. 3.1.1 Self-Similar Stochastic Fractals A self-similar stochastic fractal a(x) is characterized by the equation a(xx + h(x2 - xi)) - a(xi) = hH[a(x2) - a(x*i)], for some h, H 6 3ft, h > 1 (see appendix B). When a(x) is a random field f (x), its description can be recast into more conventional form. This reformulation 37 allows description of various properties in both the spatial and the frequency domains. Such a treatment shows that such fractals are special cases of self-similar random fields. a) Self-similar covariance functions Theorem 1: A stationary random field has a covariance function C(x) such that within some range A < \x\ < A C(hx) - C{6) = h2H[C{x) - C(0)]; h, H e 9c, h > 1 iff within that range the field behaves as a stationary stochastic fractal, with scaling ratio h, and similarity parameter H . Proof: If the random field f ( x ) is stationary, the behaviour of its increments can be described by f ( x x + h(x2 - x \ ) ) - f ( x x ) = w(h,H,x)[f ( x 2 ) - f ( x i ) ] where x\ and x*2 are arbitrary points, x = x 2 — X\, and w(h,H,x) is a function as yet undetermined. Taking the variance of both sides and using the symmetry of the covariance function yields [C{hx) - C(0)] = w{h,H,xf[C{x) - C(0)]. When A < | x | < A, w(h, H, x) can be identified as hH; the random field therefore exhibits fractal behaviour in this range. Conversely, if the field exhibits fractal behaviour, its increments are such that f ( x : + h(x2 - x x ) ) - f ( x x ) = hH[f{x2) - f ( X i ) ] ; A < | x | < A. 38 C ( x ) C ( x ) A A (a) H = 0.85, h->l A A (b) H = 0.85, h = 2 Figure 3.1: examples of self-similar covariance functions Taking the variance of both sides leads to a covariance function of the appro-priate form. • Thus, a stationary random field has self-similar fractal behaviour iff its co-variance function is of the form given in theorem 1. Figure 3.1 shows a few possible shapes for C(x) in the one-dimensional case. In higher dimensions, the random field is not necessarily isotropic, for C(x) need not be rotationally symmetric. Indeed, the values of h and H may vary as a function of the direction of the displacement x. The general case, however, is not developed here. Instead, the random fields are assumed isotropic. The upper and lower cutoff scales are denoted by A and A respectively. Taking the limits A —*• 0 and A —• oo, the field becomes a true stochastic fractal, self-similar over all spatial scales. Given some initial displacement x0, it follows from self-similarity that the difference C(x) — C(0) is proportional to 39 h2H} for displacements of the form x = h'x0, j G Z. As j —»• oo, this difference increases without bound, forcing C(x) to become increasingly negative (figure 3.1). Since a covariance function is subject to the constraint that the variance C(0) > |C(x)| [Papo84,ChlO], it follows that a random field with true self-similar behaviour must have an infinite variance. To avoid such divergences in any physical realization, the fractal behaviour of a texture must be limited to some finite spatial range. This can be achieved by multiplying a true self-similar covariance function by a window function wp(x), where p > 0 is a measure of the window size. The function wp(x) may take such forms as e - a N 2 o r sinc(/?|x|), where a,(3 G 9c > 0. By appropriate choice of window parameters, self-similar behaviour of arbitrary accuracy can be achieved in any finite spatial range |x| < A. At scales below this range, where theorem 1 still holds, the random field may be be considered a true fractal. b) Self-similar power spectra Theorem 2: Let f(x) be a stationary ra-dimensional random field with a power spectrum S(k) bounded above by A\k\~n~2H + n6(0) for some A,H,r) G 9c, 0 < H < 1. If S(k) approaches a form such that [S{hk) - 7 (^0)] = h -n-2H [s(£)-7*(o)]; h>i for some 7 G 9c, then the behaviour of f (x) approaches that of a true stochastic fractal, with scaling ratio h, and similarity parameter H. Proof: Consider the function a 40 The central area < a of S(k) has been deleted, and its Fourier transform obtained. This transform can be rewritten as a function of radial distance k — \k\ and n — 1 angular parameters. Since S(k) < A\k\~n~2H, its integral over the n — 1 angular parameters is bounded by Bk~2H~x, where B G 3t is some finite number. When a > 0 and H > 0, the integral over radial distance k is also finite. Since Sa(0) is finite, Sa[x) must exist for all x. Subtracting the term Sa(0) and rescaling yields Sa{hx) - Sa{0) = h~n r S(k/h)[exp{i2n{x-k)} - l]dk. J ha Due to the term [exp{i27r(x • k)} — 1], the contribution of 76(0) to any integral is zero. Since ^6(0) does not influence later developments, the value of 7 may conveniently be set to zero. Substituting the term hN+2HSa(k) — Sa(k/h) into the above integral then leads to Sa{hx) - 5o(0) = h2H[Sa{x) - 5o(0)] - h2H S{k)[exv{i2n{x • k)} - l}dk. J a This last term describes the error from true self-similarity. Owing to the symmetry of S(k), the sine component of Sa(x) is zero. The exponential can therefore be replaced by a cosine. For a\x\ « 1, the magnitude of the error term obeys the inequality \h2H / a a f c S(k)[cos(27rx • k) - l]dk\ < \h2H f?h S(k){2irx • k)2dk\ < \87rn+lh2H f^swdxikyk^dk] < |87r'l+1fe2ifA/0a',Jk-n-2if(|x|fc)2A;"-1(fJfc| = 8nn+1A\h2 - h2H\{2 - 2JH r)-1a2"2 / f|x|2. This last result shows that the deviation in Sa(x) from true self-similarity has an upper bound that goes as the square of the distance from the origin. For 41 any given amount of error, then, a spatial range \x\ < A can be found within which Sa(x) has asymptotically self-similar behaviour. Decreasing the value of a reduces the size of the error term, since H < 1. The range of self-similarity behaviour shown by Sa(x) correspondingly increases. Since lim5«(x) = R(x) = C{x) + fj.2, a—»0 it follows that the behaviour of C(x) approaches \C{hx) - C(0)] = h2H[C{£) - C(0)]. Theorem 1 may then be invoked to show that the field exhibits self-similar fractal behaviour within a range that increases without bound as a —* 0. As noted above, the value of 7 has no effect upon self-similarity. Since the mean ju of a random field contributes only a term (J?S(0) to the power spectrum (see appendix A), this implies that the self-similar behaviour of the field is indifferent to the value of its mean. • For a one-dimensional power spectrum such that [S(hk) - 7*(0)] = h-^lSik) - 7^(0)], it follows from theorem 2 that the corresponding random field is a self-similar fractal with similarity parameter H. When h —*• 1, the fractal becomes self-similar under all scaling ratios. Setting 7 = 0, this reduces to Mandelbrot's result, which states that S(k) oc k~x~2H (see appendix B). Any self-similar power spectrum of the form given in theorem 2 will cor-respond to a stationary stochastic fractal. Examples of such generalized one-dimensional spectra are shown in figure 3.2. 42 (a) H = 0.20, h -> 1 (b) H = 0.00, h = 2 Figure 3.2: examples of self-similar power spectra Note that the scaling ratio h can vary independently of the similarity pa-rameter H, and that their values can be common to a wide variety of spectral shapes. 3.1.2 Self-similar Noises The restriction 0 < H < 1 in theorem 2 stems from the requirement that the corresponding random field exhibit fractal behaviour. If this requirement is dropped, the only constraint governing H is that the covariance function C(x) exists. Since the power spectrum remains self-similar, it follows that some form of self-similarity must also exist in the random field. Theorem 3: Let f(x) be a stationary ra-dimensional random field with a power spectrum S(k) bounded above by A\k\~n~2H + n6(0) for some A,H,rj £ 3t, with —n/2<H<0. If S(k) is the linear combination of a finite number of monotonic 43 functions, and is such that when \i = 0 [S(hk) - i6(0)} = h -n-2H [S(fc)- 7 £(0)]; h>l, for some 7 (E 9c, then f (x) has a covariance function C(x) such that \C{hx) - 7] [C(x)- 7]; x^O for any value of the mean \i of the field. ~ —* Proof: To show that C(x) = S(x) exists, consider the case where S(k) is a monotonic self-similar function of Partition fc-space into the regions: i) \k\ < a —* ii) \k\ > a, k{ < a, « = l,2,...,n m) ki > a, i = 1,2, ...,n —* where the A;,- are the components of k, and a 6 9c is some positive value. Since must also be finite. The value of S(k) over region (ii) is bounded from above by A\k\~n~2H. Since S[k) is integrated over a finite range of fc-space, and |A;| > a, the contribution from region (ii) is finite as well. The contribution from the third region can be expressed as H < 0, the integral of S(k) over region (i) is finite. This implies that 44 Let the n components of the displacement vector x be represented by Xi,x2,... ,£„. Owing to the rotational symmetry of S(k), there is no loss of generality by assuming that all components of x are non-zero. Using the sym-metry of the cosine function, the contribution of region (iii) can then be written roo roo I ... I S(k) cos(xiA;i)... cos(xnA;n)(iA;i... dkn. J a J a When —n/2 < H, S(k) becomes a bounded, monotonically decreasing func-tion in this region. Since k2 = Z),A:2, monotonicity also holds for each single component A;,-. The integral along any dimension i, roo J S(k) cos(xiki)dki is therefore finite, owing to the monotonicity of S(k) and the periodic symmetry of cos(xfc) about zero when x ^  0. The entire integral over all n dimensions must therefore also be finite. Since the contributions of all three regions are finite when x ^  0, S (x) must exist for all non-zero x. By the linearity of Fourier transformation, the Fourier transform S(x) must also exist if S(k) results from the linear combination of several monotonically increasing or decreasing functions. To obtain the formal relation between the self-similar behaviour of S(k) and C(x), consider the case it — 0. The formal relation between S(k) and C(x) (see appendix A) yields C0(x) -1 = J[S0(k) - i6(0)) exp{i'27r(x • k)}dk, where the subscript denotes that this holds only for the case n = 0. Rescaling by some h > 1 leads to C0{hx) - 7 = h~nJ[S0 - i6{0)]{k/h) exp{i27r(x • k)}dk. 45 Direct substitution of [S(k) - 76(0)] = h,-n-2H[S{k/h) - 76(0)] into this expres-sion yields the result for \i = 0. The effect of setting the mean /z of a random field to a non-zero value is to add a term n26(0) to the power spectrum S0(k) (see appendix A). The covariance function of the field is (see appendix A) C{x) = j S(k)exp{i2n(x-k)}dk - fj,2, so that C ( x ) - 7 = f[S{k) - {7 +»2)6{0)}exp{i2ir(x-k)}dk = J[S0(k) - 76(0)] exp{t'27r(x • k)}dk = C 0 (x ) -7 . This shows that the mean fi of the random field has no effect on the self-similar behaviour of the covariance function. • Thus, when the similarity parameter H has the values — ra/2 < H < 0, a different type of self-similar random field results. These fields do not exhibit true fractal self-similarity: their covariance functions have the same type of self-similarity as their power spectra. Such random fields were first brought to attention by Mandelbrot [MaNe68], under the name of fractional Gaussian noises. The fields developed here are a generalization of these. They will be referred to as self-similar noises. Fractional Gaussian noise is a one-dimensional random field, or stochastic process. Its power spectrum has the form S(k) = C/r|A;| - 1 - 2 J 9 r , where CH is an arbitrary constant, and —1/2 < H < 0 (see appendix B). The scaling ratio h approaches unity, so that the power spectrum is self-similar over all scales. The 46 Fourier transform of Cg\k\ 1 2H can be evaluated via [GrRy65:3.762] J x~1+p cos(ax)dx = a_/3r(/3) COS(/?TT/2); 0 < (3 < 1 to yield a covariance function of the form c W = r ( 2 g + i ) l ( - g , ) ' " ' " , ; "5<*<*>• The self-similar behaviour of fractional Gaussian noise therefore agrees with the general results of theorem 3. 3.1.3 Effectively Self-Similar Textures In general, any physical structure exhibits self-similar behaviour only within a certain range of spatial scales. For example, a coastline cannot have a definite structure at scales less than the size of a grain of fine sand, and is limited at the other extreme by the size of the Earth. The concept of true self-similarity must therefore be replaced by the notion of effective self-similarity : the similarity of measurements made over a limited range of scales. If this range falls well within the limits set by the upper and lower cut-off scales A and A, there will be little difference in measurements made on true and effectively self-similar structures. This notion of effective self-similarity can be used to develop a general class of random fields. The power spectra characterized by \S[hk) - 7^ (0)] = h-n-2H[S{k) - i6{0)] correspond to well-defined random fields when — n/2 < H < 0 and 0 < H < 1. Outside this range, their Fourier transforms do not necessarily exist. If the power spectrum is required to be self-similar only between the limits (j and fi, however, this consideration does not apply — if S^ A;) approaches zero quickly 47 enough, as k —• 0 and k —• oo, the similarity parameter 7J may take on any real value, positive or negative. Such spectra describe a general class of effectively self-similar random fields. The effectiveness of this self-similarity has an obvious dependence on the cut-off scales of the spatial structure being measured and the resolution of the measuring function used. For the human visual system, the greatest sensitivity to spatial frequency lies within the range 0.1 cyc/deg to 30 cyc/deg [CaRo68]. To be effectively self-similar, then, the random field need only have a power spec-trum self-similar over this range of frequencies. Workers in computer graphics have discovered that such effectively self-similar fields are perceived as having much the same qualitative structure as fractals and self-similar noises [HaBa84]. 3.2 Texture Generation While suitable for the generation of many self-similar random tex-tures, the fractal-generating algorithms described in the literature (e.g., [Mand75][FoFu82][HaBa84]) are not flexible enough to allow independent vari-ation of all stochastic parameters of interest. More conventional techniques for producing random textures (e.g., [PrFa78][Scha80][Gaga81]), on the other hand, allow virtually complete control of statistical properties, but their specifications have usually lacked a direct connection to fractal properties. Such a connection, however, has been established in section 3.1, where various characteristics of self-similar random fields have been cast into terms involving covariance functions and power spectra. This provides a basis for generating self-similar random tex-tures via the Fourier transformation of random variables. The Fourier approach has been used previously to generate conventional textures (e.g.,[Scha80]) as well 48 as the Fourier-Brown-Wiener fractals [Mand82], but the framework established here allows production of a much larger class of self-similar textures. 3.2.1 Basis of the Algorithm The generation of a one-dimensional Gaussian random field f (x) can be based on Fourier transformation, viz., /oo m(fc)z(fc) exp{i27rA;x}d/:, -oo where the z(k) are a one-dimensional field of identical, independent, zero-mean, delta-variance Gaussian random variables, and m(k) is a modulating function. The power spectrum of f(x) is S(k) = |m(A;)|2; its covariance function C(x) is the Fourier transform of S(k). The field f(x), being a linear combination of independent Gaussian variables, is a stationary, zero-mean Gaussian random field, completely specified by C(x) [Papo84]. This entails that f (x) is completely specified by m(k), an easily-controllable quantity. The generation of random self-similar textures, as developed here, is based on this result. Various one-dimensional random fields (i.e., stochastic processes) can be generated by specifying different forms for m(fc). The resulting images then need only be swept down through a finite distance to produce the line textures. In what follows, only continuous functions are discussed. Any physical real-ization of an image, however, must be both bounded and discrete; its spectral representation must have a similar constraint. In Appendix C, it is shown that self-similarity can be effectively captured by discrete images. Textures can therefore be generated by the Fourier transformation of discrete unit-variance Gaussian random variables modulated by a discrete function m*. 49 3.2.2 Specification of Statistical Properties A wide variety of random fields have a power spectrum S(k) such that [S{hk) - 7^ (0)] = h-1-**^) - 7^ (0)]. For the case h —> 1, S(k) must be proportional to k~l~2H. For other values of h, its form is underconstrained, and various statistical properties can be specified independently of H and h. The shape of <S"(A:) may be specified by using a fixed template function P(k) to describe its values over the range \l,h), where h > 1 is to be the scaling ratio. This pattern is then repeated for all intervals [h*, h'+1),j 6 Z, with P(k) being geometrically scaled up or down by hS~l~2H^', and its argument adjusted accordingly. This leads to a spectrum of the form s(k) = c P d * ! / ^ ^ " 1 " " 0 ' ; h? < \k\ < h3+1, where j is the integer denoting the particular interval, and c is an arbitrary positive constant. The template function P(k) is an arbitrary bounded function, constrained to be positive. The resultant spectrum is obviously self-similar; an example is given in figure 3.3. From theorems 2 and 3, such a power spectrum describes a fractal when 0 < H < 1, and a self-similar noise when —1/2 < H < 0. The square root m(k) of S(k) is the modulating function required to generate the random field f(x). By suitable design of S(k), therefore, several statistical properties can be independently specified. Among these are: 50 S(k) H o k 1/h 1 h Figure 3.3: example of template construction Similarity parameter The similarity parameter H, closely related to the fractal dimension D (see appendix B), can have any real value. The choice of H determines whether f (x) is a true fractal, a self-similar noise, or an effectively self-similar random field. Scaling ratio The scaling ratio h can be given any value greater than unity. In the limit h —> 1, it leads to a random field self-similar for any scaling ratio, i.e., S(k) oc k~x~2H. Otherwise, it may be specified independently of H and P{k). Variance All self-similar textures ideally have an infinite variance. In practice, however, any random field is only effectively self-similar, having a finite variance. This quantity can be varied by altering the value of c. 51 Moments Different moments of S (k) — or equivalently, of C(x) — can be obtained by changing the form of the template function P(k). Again, this can be done without altering the values of the similarity parameter and scaling ratio of the random field. 52 Chapter 4 Texture-Discrimination Experiments This chapter describes the psychophysical experiments carried out to determine aspects of self-similar random textures relevant to human visual perception. The experiments used a two-alternative forced-choice (2AFC) method to mea-sure the discriminability of line textures taken from different parent ensembles. 4.1 General Format 4.1.1 Subjects Two volunteers participated in the experiments. Subject A, the author, had vision corrected to normal. Subject B had uncorrected normal vision, and was unaware of the purpose of the experiments. 4.1.2 Stimuli and Apparatus All stimuli were composed of three rectangular line textures, placed one above the other (figure 4.1). The dimension of each rectangle was 128 x 256 pixels, or 7.5 cm x 15 cm. A dark border of width 16 pixels surrounded each texture, 53 Figure 4.1: display format separating it from its neighbours. This array was surrounded by a uniform field with a luminance equal to the average value of the textures. Each display contained line textures from the two classes being investigated. Two of the three textures came from the same class (i.e., they were generated using the same spectral parameters), while the third was an instance of the other random field. A set of 20 different instances was generated for each class. Selection and positioning of the instances in a given display were done randomly, subject only to the constraint that textures from the same class be adjacent. This meant that the task of the subject was to pair the middle texture with either the top or the bottom texture. Stimulus patterns were displayed on a Hitachi HM-2719B-C-11 monitor. Each texture had an average luminance of 30.0 cd/m2 and contrast of 0.7 (see appendix C). The distance from the subject to the screen was set to approxi-54 mately 200 cm, so that each texture subtended an angle of 2° X 4°. The textures consequently had half-power bandlimits of 0.12 cyc/deg and 31.9 cyc/deg (see appendix C). 4.1.3 Presentation At the start of an experiment, subjects were presented with a display similar to the stimulus pattern, but with rectangles of uniform intensity in place of the textures (figure 4.2(a)). The luminance of these rectangles was set equal to the average luminance of the textures. This minimized any effects of sudden luminosity changes in the display when line textures replaced the rectangles in a presentation. Each presentation of a stimulus pattern was preceded by an acoustic warning signal. This was followed by a visual warning signal: a one-second flash of four white squares on the display (figure 4.2(b)). One second later, the stimulus pattern was displayed (figure 4.2(c)). The presentation lasted for five seconds, after which the line textures were replaced by the original uniform rectangles. Subjects were then asked whether the upper or lower pair of textures appeared more similar. Following standard psychophys-ical practice [GrSw66], they were subsequently informed of the correctness of their response by the experimenter. To avoid biasing the responses, the exper-imenter did not know the correct answer until after the response of the subject had been recorded. The duration of stimulus presentation was found not to affect the perfor-mance of the subjects. Several presentation intervals, ranging from 3 seconds to 9 seconds, were tested. Performance did not vary significantly. 55 (c) line textures (d) uniform rectangles Figure 4.2: presentation sequence 56 To measure the discriminability between two classes of texture, subjects were given a series of consecutive presentations, each involving randomly-selected el-ements of the two classes. To accustom subjects to this format, a set of learning trials was first performed. Subjects were then given sets of 50 consecutive pre-sentations, each set done in a single sitting. For most textures, two sets of trials were used, each set tested on a different day. Discriminability of the two ran-dom fields was measured by the fraction of correct responses in the combined set. 4.2 Similarity Parameter The first set of experiments was designed to measure the discriminability thresh-old AHg for textures of different similarity parameter H. When 0 < H < 1, this quantity is closely related to the fractal dimension D (see appendix B) of the texture, viz., D = 3 - H. These experiments determined the discrimination thresholds AHe of sev-eral self-similar textures with h —> 1. In accord with common psychophysical practice [GrSw66], this was taken to be the difference in similarity parameters separating textures distinguishable 75% of the time. 4.2.1 Procedure A set of self-similar line textures was generated for each of four reference classes. These textures had H £ {—1/2,0,1/2,1} and h —• 1. Each reference class was tested against eight classes of comparison textures with similar h but different 57 H. The similarity parameters of the comparison sets were greater than those of the reference classes, and were separated in steps of 0.025 from each other. The discriminability between each reference and comparison class was de-termined in the fashion outlined in section 4.1. Fifty presentations were given to each subject for every pair of classes tested. Examples of two different com-parison classes used against reference class H = 0 are shown in figure 4.3. To test for symmetry of discriminability about the reference values, a second set of observations was collected from subject A. This set was similar to the first, except that the similarity parameters of the comparison classes were below the reference values. 4.2.2 Results and Discussion The eight points obtained for each reference class were used as the basis for a psychometric function xp(H,AH), describing the percentage correct identifi-cation as a function of the reference value H, and the difference AH. Probit analysis [Finn7l][McKl85] was used to determine the threshold values (75% cor-rect identification) for each of these curves. By convention, AH is taken to be positive for comparison textures with values of H higher than those of their reference classes. Results are shown in table 4.1. Performance was consistent for both subjects. The chi-square values were calculated, and used to determine the quantity p, the probability of error in the fit of the curve. The values of p show that the ip(H, AH) calculated from the data have tolerably good fits to lognormal form. The threshold value AHg was found to generally decrease with increasing H. Results were similar for both positive and negative thresholds, except that 58 Upper texture: H — 0.00, h —• 1 Middle texture: H = 0.30, h -» 1 Lower texture: If = 0.30, h —• 1 A H = 0.30 > A-H* = 0.17 Discriminability = 88% (a) textures above threshold difference AHg Figure 4.3: line textures above and below discrimination threshold 59 Upper texture: H = 0.00, h —• 1 Middle texture: H = 0.00, h 1 Lower texture: H = 0.10, /i —» 1 Aif = 0.10 < AH9 = 0.17 Discriminability = 56% (b) textures below threshold difference AHe Figure 4.3 (continued) 60 the negative thresholds tended to have somewhat lower magnitudes. These results have two major implications. First, there appears to be no appreciable perceptual distinction between fractals and self-similar noises. The positive and negative thresholds for H = 0 involve fractals and self-similar noises respectively; no large difference was found in their values. Furthermore, the threshold values for the self-similar noise (H = —1/2) showed no great deviation from the general pattern common to all other self-similar textures. The results also show that line textures that have an effective spectral self-similarity behave in much the same way as do true self-similar textures. The two discrimination thresholds for H = —1/2 have nearly similar values, even though the negative threshold was measured using textures with H < —1/2. Similarly, the positive threshold for H = 1 has a magnitude virtually equal to that of its negative counterpart, in spite of being based on comparison textures with H > 1. In summary, then, the results of this experiment show that self-similar line textures with h —• 1 give rise to a continuum of perceived textures. No large changes in discriminability occur when stimuli change from fractals to self-similar noises to effectively self-similar textures. 4.3 Scaling Ratio The scaling ratio h is the minimum factor by which a self-similar texture can be compressed or expanded to match itself statistically (see appendix B). Con-sequently, such a texture will also match itself under rescaling by factors of h? y j E Z. A second set of experiments was carried out to measure the sensitivity 61 discrimination thresholds — subject A H positive AHga Pb negative AHe p -0.5 0.19 ±0.03 0.01 0.22 ±0.04 0.33 0.0 0.17 ±0.02 0.07 0.22 ±0.04 0.25 0.5 0.17 ±0.02 0.27 0.20 ±0.03 0.19 1.0 0.10 ±0.02 0.07 0.07 ±0.01 0.33 discrimination thresholds — subject B H positive AH$ P negative AHe P -0.5 0.17 ±0.02 0.38 _ c — 0.0 0.15 ±0.02 0.38 — — 0.5 0.17 ±0.02 0.22 — -1.0 0.10 ±0.02 0.60 - -"(tolerances are for 5% error) '(probability of deviation from lognormal form) c(dash indicates experiment not performed) Table 4.1: discriminability of similarity parameter H 62 of the human visual system to this quantity when H was kept fixed. As was shown in chapter 3, the form of S(k) for a self-similar random field is not uniquely specified by H and h when h > 1. To examine the discriminability of h, arbitrary choices must be made for the shape of the pattern function P(k). Several different shapes - hopefully representative - were investigated. 4.3.1 Procedure A set of reference textures {UJJ} with h —* 1 was generated for the values H G {—1/2,1/2}. These textures have power spectra SuH[k) oc k~1_2H, and are therefore spectrally self-similar over all scaling ratios. Each set served as a reference for comparison against other textures of similar H. Discriminability was measured using one hundred presentations for each pair of texture classes compared. A set of comparison textures Auh was generated for each of the similarity parameters H G {-1/2,1/2}, and scaling ratios h G {1.73,2.0,3.0,4.0}. The spectrum SAHh(k) of these textures was based on a template function of the form PAH<M = - hk»rY\i - c o s c ^ - ^ p ) ] ; k*r < k < her, where the base frequency k^se was set to 1/128 cyc/deg. Zeroes of SAlih(k) consequently occurred at the frequencies h'k^se, j G Z. Such a power spectrum is a series of relatively narrow peaks that increase in width and spacing as k —•oo (figure 4.4(a)). Both subjects were also tested on a second family of comparison textures {BH,?I}- The power spectrum SBHh(k) of these textures was similar to SAah(k), 63 S ( k ) O 8 1 6 24-k ( c y c / d e g ) (a) power spectrum SAfIh(k) for H — —1/2, h = 2.0 o a 1 6 2 4 k ( c y c / d e g ) (b) power spectrum SBHyh{k) for H = —l/2,h — 2.0 O 8 1 6 24-k ( c y c / d e g ) (c) power spectrum SCHh{k) for H = —1/2, h = 2.0 Figure 4.4: power spectra for {An,h}, {BH,h}, and {Cnih} 64 but with kb£at = so that the peaks of SBlth (k) fell midway between the peaks of SBHh(k). Figure 4.4(b) shows the spectrum SBHh(k) for H — 1/2, h - 4.0. The discriminability of a third family of textures {Cff,h} was also tested using subject A. These textures were similar to the {AH^}, but with a template function of the form pcBA(k) = - c o B ( 2 ( l-i )tp)] ; kcae^k<hkcse> where kg" = kb^ae. The power spectrum ScH>h{k) for H = 1/2, h = 4.0 is shown in figure 4.4(c). Since kg86 = kb^se, the zeroes of these two functions coincide. 4.3.2 Results and Discussion Results are shown in Table 4.2. Again, performance was consistent for both subjects. In all cases tested, textures became more discriminable as the differ-ence in h increased. The exact shape of the template function did not greatly affect the results. Textures with H = —1/2, however, were more discriminable for a given difference Ah than were those with H = 1/2. These results show that the similarity parameter H — or equivalently, the fractal dimension D — is not the only second-order quantity relevant to dis-criminability. Although having little effect when H = 1/2, the value of h does influence discriminability when H = —1/2. Interestingly, the scaling ratio does not have a large effect on discriminability when its value is two or less. White noise (H = —1/2, h —• 1) is virtually indistinguishable from textures with power spectra composed of pulses spaced 65 H = -l/2 percentage correct a — subject A h {UH} vs {AHIH} » {UH} VS {BH,H} {UH} VS {CHTH} 1.73 64 ±4.8 55 ±5.0 56 ±5.0 2.00 70 ±4.6 61 ±4.9 55 ±5.0 3.00 80 ±4.0 69 ±4.6 64 ±4.8 4.00 93 ±2.5 91 ±2.9 72 ±4.5 H = -1/2 percentage correct — subject B h {UH} vs {AH,H} {UH} VS {BH,H} {UH} VS {CHTH} 1.73 61 ±4.9 50±5.0 _ c 2.00 68 ±4.7 61 ±4.9 3.00 78 ±4.1 76 ±4.3 4.00 92 ±2.7 93 ±2.6 — "(tolerances represent ±1 standard error) '(reference {UH} has h —• 1) c(dash indicates experiment not performed) Table 4.2: discriminability of scaling ratio h 66 H = 1/2 percentage correct a — subject A h {UH} vs {AHIH} B {UH} VS {BH,H} {UH} VS {CH,H} 1.73 52 ±5.0 49±5.0 47 ±5.0 2.00 61 ±4.9 62 ±4.9 59 ±4.9 3.00 69 ±4.6 62 ±4.9 67 ±4.7 4.00 77 ±4.2 64 ±4.8 69 ±4.6 H = 1/2 percentage correct — subject B h {UH} vs {AH,H} {UH} VS {BH,H} {UH} VS {CH,H} 1.73 53 ±5.0 58±4.9 _c 2.00 56 ±5.0 52 ±5.0 — 3.00 61 ±4.9 64 ±4.8 — 4.00 61 ±4.9 59 ±4.9 -"(tolerances represent ±1 standard error) '(reference {UH} has h —• 1) c(dash indicates experiment not performed) Table 4.2 (continued) 67 apart by a factor of two; when the pulses are spaced apart by a factor of four, however, discriminability is increased dramatically (figure 4.5). 4.4 Discriminability of Other Properties To examine whether the second-order quantities H and h are sufficient to char-acterize the perception of self-similar random textures, a final set of experiments measured the discriminability of textures with identical H and h, but with dif-ferent template functions P(k) for their power spectra. 4.4.1 Procedure The textures in the families {AJJ^}, {BH,H}, and {CH,K} were tested against each other for all combinations involving similar H and h. Subject A made the full range of observations H G {-1/2,1/2}, h G {1.73,2.0,3.0,4.0}. Subject B was tested on the range H G {—1/2,1/2}, h G {2.0,4.0}. Again, each pair of classes was tested using one hundred presentations. To determine the effect of a different compression of the power spectrum, a fourth family of textures {Djj>4} was generated for H G {—1/2,1/2}, h = 4. These had the same form of template function as the {Aji,h}, but with kpse set to §A T^e. These sets were tested for discriminability against the classes AJJ^ and C H i 4 . 4.4.2 Results and Discussion Results are given in Table 4.3. Performance of both subjects again remained consistent. Textures with similar H and h were found to be discriminable. This 68 Upper texture: Bjj,h, H = —0.50, h = 4 Middle texture: Ug, H = —0.50, h —* 1 (white noise) Lower texture: UJJ, H = —0.50, h —• 1 (white noise) Discriminability = 91% s ( k ) O 8 1 6 2 4 k ( c y c / d e white noise {BHh} (a) comparison of white noise and {BJJ^} for H = —0.5, h = 4 Figure 4.5: white noise vs {BH,H} 69 Upper texture: UH, H = - 0 . 5 0 , h —• 1 (white noise) Middle texture: UH, H = —0.50, h —• 1 (white noise) Lower texture: Bn,h, H = —0.50, h = 2 Discriminability = 61% S ( k ) white noise (b) comparison of white noise and {BHih} for H = —0.5, h = 2 Figure 4.5 (continued) 70 discriminability generally increased as H decreased or h increased. As in the previous experiment, discriminability was always low when h was two or less. These results show that H and h are not the only second-order quantities relevant to the perception of self-similar random textures. Other quantities, dependent on P(k), must also be involved. This is consistent with the obser-vation that when h —» oo (i.e., when the texture is no longer self-similar), the function P(k) completely describes the power spectrum. From the low discrim-inability between textures with h < 2, however, it follows that a difference in the template functions does not generally suffice for high discriminability of the corresponding textures. 71 H = -1/2 percentage correct 0 — subject A h {AH,h} vs {BH,h} {BHIH} VS {C h,4 {CH,h} vs {AH^} 1.73 51 ±5.0 53 ±5.0 48 ±5.0 2.00 50 ±5.0 65 ±4.8 75 ±4.3 3.00 85 ±3.6 87 ±3.4 76 ±4.3 4.00 97 ±1.7 86 ±3.5 99 ±1.0 h {AH,h} vs {DHIH} {DHIH} vs {CHth} _ 4 4.00 98 ±1.4 57 ±5.0 — H = -1/2 percentage correct — subject B h {AH,h} vs {BHIH} {BH,H} VS {CH>h} {C„,h} VS {AH,k} 2.00 70 ±4.6 56 ±5.0 67 ±4.7 4.00 100 ±0.0 88 ±3.2 97 ± 1.7 h {AH,H} VS {£>*,„} {DH,K} VS -4.00 100 ±0.0 60 ±4.9 -"(tolerances represent ±1 standard error) '(dash indicates experiment not performed) Table 4.3: discriminability of template function P(k) 72 H = 1/2 percentage correct ° — subject A h {AHih} vs {BHih} {BHth} vs {CHth} vs {Atf.J 1.73 47 ±5.0 46 ±5.0 50 ±5.0 2.00 54 ±5.0 65 ±4.8 54 ±5.0 3.00 69 ±4.6 58 ±4.9 76 ±4.3 4.00 78 ±4.1 76 ±4.3 73 ±4.4 h {AH,h} vs {DH>h} {DHth} vs {CH>h} _ b 4.00 77 ±4.2 53 ±5.0 — H = 1/2 percentage correct — subject B h {AH,h} vs {BHth} {BH,h} vs {CH<h} {CH,h} vs {AHih} 2.00 47 ± 5.0 55 ±4.9 51 ±5.0 4.00 58 ±4.9 81 ±3.9 76 ±4.3 h {AHih} vs {DH,h} vs {C*,*} -4.00 73 ±4.4 59 ±4.9 -"(tolerances represent ±1 standard error) 6(dash indicates experiment not performed) Table 4.3 (continued) 73 Chapter 5 Discussion The central goal of this work is to determine aspects of self-similar random textures perceived by the human visual system. Results from the preceding chapter show that both the similarity parameter and the scaling ratio have an influence on perceived texture, but that they are insufficient to characterize it completely. Some implications of these results for a general model of texture perception are now examined. Since neither the texton theory nor the symbolic-structure approach are sufficiently developed to allow quantitative predictions to be made about the discriminability of random line textures, discussion is focussed on the relevance of the results for spatial-frequency models. The quantitative nature of these models allows the results to be checked for compatibility with the hypothesis that texture perception is mediated by measurements made on each of several parallel spatial-frequency channels. 74 channel peak frequency (cyc/deg) h oi(deg) <r2(deg) a3(deg) A 0.8 0.267 0.000 0.198 0.593 0.000 B 1.7 0.333 0.000 0.098 0.294 0.000 C 2.8 0.894 0.333 0.084 0.189 0.253 D 4.0 0.894 0.333 0.059 0.132 0.177 E 8.0 1.266 0.500 0.038 0.060 0.076 F 16.0 1.266 0.500 0.019 0.030 0.038 Table 5.1: values of constants for spatial-frequency channels 5.1 Spatial-frequency Channels For concreteness, the channels proposed by Wilson and Gelb [WiGe84] are used in the analysis of the results. In this formulation, six parallel linear channels are postulated, with each channel t based on an isotropic filter -ff,(fc) of the form Hi(k) = a7r 1 / 2[oi exp{-(7rcr1A;)2} - foa2 exp{-(7ra2A;)2} + /?3e73 exp{-(7ra3A:)2}], where k denotes the magnitude of the spatial frequency. The values of the /?;- and Oj, taken from [WiGe84], are given in Table 5.1. Variation of these parameters with eccentricity from the center of the fovea has been ignored. The value of a, which describes the absolute sensitivity of each channel, is not of importance here — only the relative responses of a channel to different stimuli are of concern for the present analysis. The value of a is therefore arbitrarily set to unity. Any multichannel model of texture perception must specify the measure M,-75 used for each channel i. Two sets of possible measures are considered here; they are not intended to be exhaustive. The first is the relative contrast v_lrfHf(k)S(k)dk\1/2 S{k)dk J ' where S(k) denotes the power spectrum of the unfiltered image. This quantity describes the standard deviation of each filtered image, given that the unfiltered image has unit variance. Since contrast is proportional to the amplitude of the constituent waveforms of the image, V{ is proportional to the contrast of the image filtered by Ht(k). The second measure is the zero-crossing density Z% in each channel. For a Gaussian stochastic process [Papo84,ll-4], 7 _n_(fk*Hnk)S(k)dk\1/2  1 V fH?{k)S{k)dk J ' Zero-crossing densities resulting from a set of V 2 G filters at differing spatial scales were briefly considered by Riley [Rile81], but a thorough investigation of their suitability for representing texture has never been carried out. Various metrics for the perceptual distance between two texture classes can —* —# be based on V and Z. In what follows, the only constraint placed on the postulated metric is that it be weakly monotonic: if the ensemble values of measures M,- made on texture classes T0, Ti, and T 2 are such that M,(T2) > Mi{Tx) > M,(T0) for each channel i, then the discriminability between Ti and T0 must be less than or equal to that between T2 and T0. If the measurements made do not obey such an ordering, then no prediction can be made. 76 The use of such a weak constraint entails that predictions can only be made about the relative discriminability between some of the texture classes. The advantage of this approach, however, is that no detailed mechanisms of texture perception need be specified. This provides a way to test the compatibility of the results with the general class of models based on spatial-frequency channels. 5.2 Analysis The values of V and Z for each of the reference classes H 6 {—1/2,0,1/2,1}, —# —* h —* 1 are given in Appendix D. Also given are the values V and Z for the comparison classes at the upper and lower discrimination thresholds. These values have been calculated only for subject A, since the performance of subject B was similar over virtually all textures tested. The results concerning the discriminability of H show that texture classes on the discrimination threshold generally have AVi/Vi in the range 10 — 50% for all channels. The values of AVi are roughly symmetric about the reference values for the upper and lower thresholds. This symmetry also holds for the AZ,-. The relative quantities AZi/Zi almost always fall in the range of 2 — 10%, with most values falling in the range of 2 — 6%. This last figure is interesting, since the relative difference in frequency for sine-wave gratings at the discrimination threshold is 2 — 5% [WiGe84]. The discriminability of the similarity parameter H would therefore appear to be compatible with a multichannel mechanism for texture perception. To determine whether the discriminability of the scaling ratio h is also com-—* —* patible with such a model, the values of V and Z for all comparison textures used in section 4.2 were compared against those of the threshold texture classes 77 used in the previous analysis. Assumption of a monotonic discrimination mech-anism allows two types of prediction to be made: 1. If all measures Aff- of a comparison class Ti fall between the corresponding values for one of the threshold textures Tg and the values for the reference class To, then the discriminability between Ti and To must be less than or equal to the threshold level of 75%. 2. If all measures M,- for one of the threshold classes Te fall between the corresponding values for the comparison class Ti and the values for the reference class T0, then the discriminability between Ti and T0 must be greater than or equal to the threshold level of 75%. Table 5.2 shows the predicted discriminability of the texture classes, using V —f and Z as two independent sets of measures. Comparison with the experimental results (Table 4.2) shows agreement with almost all predictions made. The sole exception occurs for H = 1/2, h = 4. Prediction based on V states that the discriminability between {UH} and {AH.H} should be below 75%. Comparison with table 4.2 shows that the observed discriminability is 77%. The standard error, however, could push this value down below the discrimination threshold, so that this exception is not statistically significant. —# The predictions — in particular, those based on V — correctly describe the diminished discriminability between textures with H = 1/2. Especially in-teresting is the result that discriminability should generally be poor between textures with h < 2.0. These predictions are not sufficiently detailed to deter-—* —* mine whether V, Z, or some combination of the two should be favoured as the set of measurements involved in texture perception. However, they do show 78 that a multichannel model remains consistent with the discriminability of the scaling ratio. This approach can also be used for texture classes that have different tem-plate functions P{k). The differences AV,- and AZ, can be used as before. If the values Vi and Z,- for the textures differ only slightly from those of the [h —>• 1) references, the same discrimination thresholds as in the previous analyses can be used as the basis of a consistency check. Table 5.3 shows the predicted discriminabilities of the texture classes. Com-parison with experimental results (Table 4.3) shows that the low discriminability of the H = 1/2 textures is correctly predicted, as is the generally low discrim-inability between textures with h = 1.73. Again, the Vi appear to be more sensitive measures of discriminability than the Zi. However, predictions are still too weak to allow one set to be preferred over the other. The low discriminability between many texture pairs cannot be predicted using this model. For example, no prediction can be made for the discriminabil-ity between CH,A and DH,A when H = —1/2. However, a comparison of V and Z for the two classes of texture (Table 5.4) shows that AV,- and AZt are generally quite small. This situation is typical of many cases where prediction cannot be made. Taken together, these results imply that the ability of the human visual system to perceive self-similar random textures is compatible with models of texture perception based on spatial-frequency channels. 79 H = -1/2 percentage correct — measure = V h {UH} vs {AH,H} A {UH} vs {BHLH} {UH} VS {CH,H} 1.73 < 75 6 < 75 < 75 2.00 c < 75 < 75 3.00 — — — 4.00 — — — H = -1/2 percentage correct — measure = Z h {UH} vs {AH,H} {UH} VS {BH,H} {UH} VS {CH,K} 1.73 < 75 < 75 < 75 2.00 < 75 — < 75 3.00 — — — 4.00 — — — "(reference {UH} has h —• 1) b(l< 75' indicates discriminability is below discrimination threshold) "(dash indicates no prediction can be made) Table 5.2: predicted discriminability of scaling ratio h 80 H = 1/2 —* percentage correct — measure = V h {UH} vs {AH,H} A {UH} vs {BH>H} {UH} VS {CHTH} 1.73 < 75 b < 75 < 75 2.00 < 75 < 75 < 75 3.00 < 75 < 75 < 75 4.00 < 75 < 75 < 75 H = 1/2 percentage correct — measure = Z h {UH} vs {AHTH} {UH} VS {BH,H} {UH} vs {CH>H} 1.73 < 75 < 75 < 75 2.00 c < 75 < 75 3.00 — — — 4.00 — — — "(reference {UH} has h —> 1) 6('< 75' indicates discriminability is below discrimination threshold) c(dash indicates no prediction can be made) Table 5.2 (continued) 81 H = -1/2 —* percentage correct — measure = V h {AH,h} vs {BH,h} {BH,H} VS {CHTH} {CHIH} vs {AHih} 1.73 b — < 75 a 2.00 — — — 3.00 — — — 4.00 — — — h {AH>H} vs {DHih} {DHih} vs {CHIH} — 4.00 — — H = -1/2 —* percentage correct — measure = Z h {AH>K} vs {BH>H} {BHTH} vs {CHTH} {CH,H} vs {AH<H} 1.73 — < 75 < 75 2.00 — — — 3.00 — — — 4.00 — — — h {AH,h} vs {DH,h} {DHth} vs {CHTK} — 4.00 — — — °('< 75' indicates discriminability is below discrimination threshold) '(dash indicates no prediction can be made) Table 5.3: predicted discriminability of template function P(k) 82 # = 1/2 —* percentage correct — measure = V h {AH,H} VS {BH,h} {BH,h} vs {CH,h} {CH.H} vs 1.73 < 75 ° < 75 < 75 2.00 < 75 < 75 < 75 3.00 < 75 < 75 < 75 4.00 b < 75 < 75 h {AH,h} vs {DH>H} {DH,H} VS {CHIH} — 4.00 < 75 < 75 — H = 1/2 —* percentage correct — measure = Z h {AH,h} vs {BH>h} {BHth} vs {CH,,} {CHlh} vs {AHTH} 1.73 < 75 < 75 < 75 2.00 — < 75 — 3.00 — — — 4.00 — — — h {AH,h} vs {DH>H} {DHIH} vs {CHTH} — 4.00 — < 75 — a('< 75' indicates discriminability is below discrimination threshold) 6(dash indicates no prediction can be made) Table 5.3 (continued) 83 H = -l/2 —* measure = V measure = Z channel {CHA} {DHA) {CHA) {DHA) A 5.40 x 10-2 5.41 x 10~2 2.22 2.25 B 3.13 x 10"2 3.02 x 10"2 4.43 4.35 C 1.93 x 10-2 1.82 x 10"2 7.76 8.24 D 2.08 x 10"2 2.14 x 10"2 9.47 9.49 E 5.33 x 10-3 4.79 x 10-3 15.87 14.03 F 6.21 x 10"3 6.45 x 10~s 37.05 37.27 Table 5.4: comparison of ensemble values for CHA a n d DHA 84 Chapter 6 Conclusions This work investigates the ability of the human visual system to discriminate between self-similar random textures. The properties of such textures are de-termined in both the spatial and the frequency domain, and their relation to the class of self-similar stochastic fractals is established. It is shown using psy-chophysical experiments that the similarity parameter H and the scaling ratio h influence the discrimination of self-similar line textures, but that they are insufficient to completely characterize perceived texture. Analysis shows that the results of the experiments are compatible with a multiscale model of texture perception. These results are relevant to three areas of study. First, they suggest new methods of texture analysis. Previous techniques for analyzing textures by their fractal properties (e.g., [Pent83][PeNa84]) have considered only the simi-larity parameter H, often using one-dimensional measures for its determination. This work shows that H is insufficient to characterize a random texture: other properties, such as the scaling ratio h, must also be taken into account. The treatment of self-similar random textures given in chapter 3 provides a basis for determining these quantities, and to do so using two-dimensional spatial and 85 spectral measures. More generally, the multichannel model of texture perception suggested in chapter 5 can be implemented on a machine. Using only the relative measure-ments in each channel, algorithms can be developed that are translationally and rotationally invariant, and are robust under changes of grey level and scale. Indeed, initial work on one such algorithm shows promising results for the seg-mentation of natural images [Litt86]. The second area of relevance is computer graphics. Self-similar stochas-tic models are widely used to represent various objects and surfaces (e.g., [FoFu82][HaBa84]). The treatment of self-similarity given in chapter 3 forms a rudimentary basis for relating these self-similar constructs to true fractals. Fur-ther, the texture-discrimination experiments described in chapter 4, although based only on self-similar line textures, determine the sensitivity of the hu-man visual system to several properties of interest. This enables an estimate to be made of whether an algorithm can generate objects that appear truly self-similar. Finally, the results of this work are of relevance to the computational study of the human early vision system. The results of the psychophysical experiments are found to be compatible with the hypothesis that texture perception is medi-ated through measurements made in parallel on the spatial-frequency channels putatively involved in form vision. This suggests that texture perception and form perception could share common mechanisms. Open Questions Although sufficient for the purposes of this work, the treatment of self-similar 86 random fields given in chapter 3 is incomplete in several respects. First, the relation between self-similar random fields and self-similar stochastic fractals established in in theorem 2 (section 3.1.1) applies only in one direction: if the —* n-dimensional power spectrum S[k) is self-similar, with 0 < H < 1, then the corresponding random field is a stationary stochastic self-similar fractal. The converse relation, however, is not established, and its existence is an open issue. Another issue also involves theorem 2. For the proof of the theorem to work, the power spectrum must be limited from above by a function A\k\~n~2H, where A is some positive number. Although presenting no constraint for any practical application, this restriction places a theoretical limitation on the type of spectral self-similarity that can correspond to spatial self-similarity. In the interest of completeness, it would be useful to establish whether the relation given by theorem 2 holds for all self-similar power spectra. Limitations on the form of S(k) also apply to theorem 3 (section 3.1.2), which relates the self-similar form of the covariance function to the self-similar form of the power spectrum when —n/2 < H < 0. Again, the boundedness required of S{k) is of no practical concern. The condition that S(k) be composed of several monotonic functions is likewise of little practical consequence. From a theoretical viewpoint, however, it would be interesting to determine whether theorem 3 would still apply if the restrictions on the form of S (k) were removed. If so, the converse of theorem 3 would follow as a natural result. Directions for Future Work The approach used in this work can be extended in several ways. First, a much wider range of discrimination experiments could be carried out, using the 87 techniques described in chapters 3-5. This would not only allow a more precise estimate of the discrimination thresholds, but would also provide additional evidence either for or against various multichannel mechanisms. Experiments involving textures with different first-order statistics (i.e., mean and variance) would also contribute toward this end. Although time-consuming, these exper-iments would be straightforward to carry out. The techniques described in chapters 3-5 could also be used to examine the multichannel hypothesis itself, without specific reference to self-similar textures. The requirement of self-similarity could be dropped, and the power spectra of texture classes designed expressly to distinguish between competing multichan-nel models. To make this approach feasible, a method is required for the design of the appropriate spectra. A more elegant route to the same goal, however, would be to develop tech-niques for determining the form of the putative spatial-frequency channels di-rectly from the observed discriminabilities. It is difficult to estimate the amount of effort required to develop such techniques. Once constructed, however, they would be of great value in determining the exact mechanisms of texture per-ception. An extension of these techniques to self-similar non-Gaussian random tex-tures would also be of interest. Such textures could be readily created, e.g., by using dithering techniques to binarize Gaussian textures. Although the theo-rems developed in chapter 3 would still hold, the analysis of the results would almost invariably be difficult, for the simplifications available for the Gaussian case are not generally applicable. However, special cases might be found for which the analysis would be tractable. These could provide useful checks on 88 A A B B A B A B (a) horizontal alignment (b) vertical alignment Figure 6.1: cross display format the results achieved for the Gaussian case. Perhaps the most obvious extension of the approach developed in this work is to apply it to fully two-dimensional textures. The theorems on self-similarity developed in chapter 3 apply to the general n-dimensional case, so that genera-tion of various two-dimensional self-similar textures would be straightforward. Discrimination experiments analogous to those of chapter 4 could be carried out via a cross display format: two pairs of textures would be aligned at random either in the horizontal or in the vertical direction (figure 6.1). This technique could be used to determine various anisotropics of the visual system. Analysis of the results, however, would be difficult — there is at present no analytic formulation of the distribution of the angles, lengths, curvatures, etc., of the zero-crossings in a two-dimensional Gaussian random field. A more reasonable approach to analyzing the two-dimensional nature of tex-ture perception might be to investigate the discriminability of line textures at various orientations. Such a study would be relatively easy to carry out, since 89 the analytical techniques have already been developed. It would be interest-ing to compare such results against those expected from fully two-dimensional spatial-frequency filters. One last suggestion for future work is to investigate the discriminability of textures of various colours. Virtually all work in texture perception has involved monochromatic textures that were broadband, i.e., black and white. It would be straightforward to do analogous experiments and analyses on narrowband textures, composed of just a few spectral colours. Of particular interest would be the discriminability of textures whose chromatic components have different dimensions. 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Among the representations commonly used are sets of average properties. These properties and their relations can be determined by the methods of random-field analysis. Such methods originated in the analysis of time series, one-dimensional random functions for which the value of a function at a given 99 t Figure A.l: example of time series time cannot be predicted exactly from the knowledge of its values at previous times (figure A.l). Although different sections of a time series v(t) over similar intervals At have little similarity in a strict sense, their average properties are often nearly identical. This leads to the idea of modelling a time series by a stochastic process, an ordered set of random variables v(t) that describes the ensemble of functions that could possibly be realized. The function v(t) is simply one of the infinitely many values that the process v(r) could have taken. Such a treatment makes it possible to relate the measured averages of v(t) to the ensemble properties of v(i). This allows the relations between the average properties to be treated in an exact fashion. To maintain the distinction between the ensemble and one of its instances, random variables are always denoted by bold-faced characters. Any particular instance is denoted by a character of standard font. The concept of a stochastic process can be extended to obtain that of a random field, a two-dimensional space of (complex) random variables f(x,y). A random image f(x, y) may then be interpreted as an instance of the ensemble of 100 functions described by f (x, y). Time series and random fields can be considered as special cases of n-dimensional random fields f (x), where x — (xi, x 2 , . . . , xn) is an n-dimensional position vector. In what follows, only the two-dimensional case will be developed, since it is of most relevance to image analysis. Analogous developments, however, can be carried out for any finite number of dimensions. A random field can be either continuous or discrete, depending on the param-eter set {x}. Continuous random fields are denoted here by standard functional notation (e.g., f (x, y), g(fc,/)). Discrete fields, on the other hand, are denoted using subscript pairs (e.g., fxy, g m „). For convenience, only the continuous case is described in this appendix. The discrete case can be developed in a paral-lel fashion by replacing integration by summation, and the continuous Fourier transforms by their discrete counterparts. A.2 Mean and Covariance A random field f(x, y) = f(x) is defined to be a set of random variables parametrized by a two-dimensional space, with each point XQ being the loca-tion of a (complex) random variable f(x*o). A random field is often represented by a multivariate probability distribution, which completely describes the joint statistical properties of all its constituent random variables. Each variable f(x 0) has an associated probability density function Pf(s0){w), where w is, in general, a complex quantity. The consideration given to complex random variables is not only for the sake of generality, but also because of the simplifications brought to the formal description of Fourier transformation. 101 Real-valued random fields are easily treated as a special case of this more general approach. Two significant properties of a complex random variable are its mean fj,(x0) = E{f(x0)} = w • Pf(Zo)(w)dw, (A.l) and its variance o2(xQ) = E{\i{x0) - n{x0)\2} = fjw- H(x-0)\2pnso)(w)dw, (A.2) where the region of integration is taken to be the complex plane 3. Pairwise correlations amongst the random variables are described by the correlation function R{x1,x2)=E{f(x1)f(x2y}, (A.3) and the covariance function C{xux2) = ^{[f(x!) - n{Si)][f{Sa) - , , (A.4) - R{x!,x2) - n{xi)n(x2)*, which describe the linear dependence of f (x*i) and f (x2) on each other. When /x(x) = //, the two functions differ only by the constant \/x\2, and it is common practice to use only one of them for description. In such a case, this work uses the covariance function C(xi, x2) to describe the second-order moments of random fields. Note that C(x1,x1) = a2{xx), i.e., the covariance of a single random variable is its own variance. If the random field has a multivariate Gaussian distribution associated with it, the field is completely specified by fi(x) and C(xi,x2) [Papo84:9-2]. For such a case, the condition C(xi, x2) = 0 implies that the corresponding random variables are independent. 102 A.3 Stationarity The random field f(x, y) is said to be stationary if its statistical properties remain invariant under translation, viz, f (x, y) = f(x + Ax, y + Ay). Such fields represent processes that are independent of any particular location — they have an equilibrium distribution that has the same statistical properties everywhere. In the remainder of this report, attention is restricted to stationary random fields. For a multivariate Gaussian distribution, it follows that the random field is stationary iff fj,(x) = fi and C(xi,x 2) = C(xi — x 2). A.4 Sample Functions Given an instance f(x,y) of f(x, y), various sample functions may be defined on it. These functions determine various average properties, which in turn can provide estimates of ensemble properties. For any given instance f(x,y) a sample average f may be defined as 1 r^/ 2 rTv/2 f = TfTjr / / f(x,y)dxdy, where the random field f (x, y) has been assumed to be zero outside the domain \—Tx/2,Tx/2] x [—Ty/2,Ty/2]. The sample correlation function is similarly de-fined as c(Ax, Ay) = —— j \ [f{x + Ax,y + Ay) - /][/(x,y) - f)*dxdy. 103 These sample functions may be considered to be particular instances of ensemble estimators. Ensemble estimators are functionals of the random field, assigning a random variable to any f(x, y). The estimator for the mean, for example, is When /(x,y) is stationary, it can be shown that the estimators f and c(Ax, Ay) asymptotically approach the constant ensemble values of n and C(Ax, Ay) respectively as Tx,Ty —> oo [JeWa68,ch5]. Both sample functions are consequently ergodic, the spatial averages reflecting the ensemble averages. A.5 Fourier Analysis The techniques of Fourier analysis can be usefully applied to the study of ran-dom fields. Attention is focussed here on the Fourier series and Fourier trans-forms of continuous functions. Analogous developments for the discrete case can be done using finite discrete Fourier series [JeWa68]. An instance /(x, y) of a random field f (x, y) may be analyzed into its con-stituent harmonics in the same way as any other function. Let /(x, y) denote an image that is non-zero only inside the domain [—Tx/2,Tx/2] x [—Ty/2, Ty/2]. Such an image can be represented by the Fourier series and the covariance estimator is oo oo /(*>!/)= E E /*,exp{*27r(»/rs + m/rw)}, n = — o o m = — o o 104 where l rT*/2 rTy/2 fki = TFTrT / , / f(x,y)exp{-i2w(kx + ly)}dxdy. l x l y J-Tz/2 J-Ts/2 The term is the frequency-space representation of f(x,y). It is almost always random for any given instance f(x,y), its value at a given point having no definite relation to the values at other points. Increasing the size of Tx and Ty does not cause fkt to settle down to some deterministic function. Apart from windowing effects arising from the finite sizes of Tx and Ty, the average properties of fa (as determined from its sample functions) usually converge to definite values as Tx,Ty —> oo. (See [JeWa68] for illustrations of the one-dimensional case.) With this is mind, it is natural to regard fa as an instance of an ensemble of possible functions fjfcj. When TX,TV —*• oo, this leads to the the random field ~ _, roo roo f{k,l) = f(k) = / / f(x,y)exp{-i2Tr(kx +ly)}dxdy. (A.5) J—oo J oo Similarly, f(x,y) can be expressed as the inverse Fourier transform of f(A;,/), viz., /oo roo „ / f(k,l)exp{i27r(kx + ly)}dkdl. (A.6) -oo J—oo A.6 Power Spectra When f(x, y) is stationary with mean ii and covariance C(xi — x2), it follows from eq (A.5) that E{f(k)} = H6(0), 105 and — I^oo / f ^ o R{x) exp{-i2n(kl • x)} exp{-«'27r(x2 • (A7x - k2))}dxldx2, where x = x^ — x2, and 6(k) is the Dirac delta function. This last term can be written J5{f(*j)f*(*2)} = J^oo f™oo R{x) exp{-i'27r(A71 • x)} exp{-i'27r(x2 • (kx - k2))}dxdx2 = R{x) exp{-i27r(jfei • x)}£(ifci - £ 2)dx = S&W*!"*,), where - r°° -, S(k)= I R(x)exp{-i2n(k-x)}dx J —oo is the power spectrum of the random field. The f(k) form a field of independent random variables. When k ^  0, they are zero-mean, and have a variance of S(k)6(0). It is often convenient to factor the f(fc) into f(k,l) = m(k,l)z(k,l) +n6(0), where the z(A;,/) are zero-mean random variables with a delta variance (i.e., —# —* —* —* E{z(ki)z*(k2)} = 6(ki — k2)), and where m(k, I) is a real-valued non-negative function that modulates them. Using this factorization, eq (A.6) can be written /oo roo / [m(k,l)z(k,l) + n6(0)]exp{i2n(kx + ly)}dkdl. (A.7) - o o J—oo A similar result holds for the discrete case, where E{zklz*k2} = 5fclfc3 [Papo84]. Using the definition = f-oo[™2$) + \n\26{0)]exp{i2Tr{k • (x\ - x2))}dk, 106 it follows that S(k) = m2{k,l) + \fi\26{0). Since the z(k, I) have delta variance, it follows that the power spectum describes the contribution of the harmonic at (k,l) to the rms power of f(x,y). An interesting relation exists between m2(k,l) and the covariance function C(xi,X2) of a stationary field. Since C{x1-x2) = R{xx - x2) - \ii\2 = S-JS{k,l) - M2£(0)]exp0-27r(£• (x\ - x2))}dk = S-oo m2(fc> 0 exp{i27r(fc • {xx - x2))}dk, the function C{xx — x2) is the Fourier transform of the quantity m2(k, I) = S(k) - \n\26(0). When the z(k, I) are delta-variance Gaussian random variables, the function f(x, y) is multivariate Gaussian, since it is a linear combination of Gaussian random variables. The random field is then completely determined by fx(x) and C(xi,x2). In this work, the z(k,l) are always taken to be zero-mean, delta-variance Gaussian random variables. This allows the power spectrum and covariance function to be equivalent descriptions of the random field. A.7 Real-Valued Random Fields If the random field f(x, y) is real-valued, then f(x, y) must equal f*(x, y). From eq (A.7), it then follows that z(k) must be conjugate-symmetric about the origin (i.e., z(k) = z*(—k)), and that fi be real-valued. It is important to note that if f(x, y) is real-valued, then the z{k) must be complex quantities with random phases. If the z{k) were real-valued, with 107 conjugate symmetry, then £;{z(fc1)Z*(fc2)} = 6{k! - k2) + 6[k! + jfe2). This relation, together with eq (A.7), entails that exp{t'27r((A;1x1 - fc2x2) + {hyi - /2y2))}<ifc1rffc2 = Ho /!L m2(fc, 0 exp{t27r(fc(x1 - x2) + /( y i - y2))}dk + 1^ JZo m2(k, I) exp{i2n{k{x1 + x2) + Z(yi + y2))}dk. The covariance function therefore depends upon X\ + x2, showing that sta-tionarity is lost when the z(k) are real-valued. 108 Appendix B Fractals This appendix provides a brief overview of some of the basic ideas of frac-tal geometry. It is an extension of the expositions given by Mandelbrot [Mand67] [MaNe68] [Mand75] [Mand82] [Mand84]. B . l Introduction The piecewise-differentiable curves and surfaces commonly used to describe shape do not adequately represent all aspects of the forms found in nature. For example, the length of a coastline is not a well-defined quantity — as the scale of measurement is made finer, small indentations and promontories previously unresolved become noticeable, thereby increasing the total length measured. As the resolution is increased, the length of the coastline tends toward infinity. In a similar fashion, the measured surface area of a rugged terrain depends on the scale of measurement, increasing without bound as the scale tends to zero. These are not isolated phenomena. Richardson showed empirically [Mand82] that jagged objects such as coast-lines could be characterized by the rate at which their length increased as a 109 function of measurement resolution. When the basic measuring-scale A used in some method is replaced by A/a, the number of the scale-lengths measured increases as aD, where D is some constant such that 1 < D < 2. Thus the total measured length L(X) can be written as L(A) = F\~D • A = FX1'0, (B.l) with F a constant depending on the particular object measured, and the method of measurement used. The parameter D holds constant over a wide range of spatial scales, reflecting the inherent jaggedness of a coastline. For D « 1, the line is smooth. As D increases, the line becomes much more jagged. For D —> 2, a coast becomes extremely rough and convoluted, with many islands and fiords. The average value of D for the world's coastlines is estimated to be 1.2 [Mand82]. This approach can be applied equally well to jagged surfaces and volumes. When the value of D is the same as the intuitive dimension of the object (e.g., D = 1 for coasts, D = 2 for surfaces), the object can be adequately described by standard Euclidean geometry. When D has a different value, the object is regarded as a fractal. B.2 Definitions Fractals are a class of mathematical objects largely developed by Mandelbrot [MaNe68][Mand82], who defines a fractal as a set whose Hausdorff-Besicovitch dimension is greater than its topological dimension. The topological dimension T is that captured by the intuitive idea of di-mension. All surfaces, for example, have T = 2. The Hausdorff-Besicovitch 110 dimension is obtained via a test function or generalized ball _ [r(i/2)]<* d  h d [ p ) ~ r(i + (d/2))"p ' ( R 2 ) where T(x) is the gamma function. The ball takes the following forms for {1,2,3}: d = 1: h1(p) = 2p d = 2: ^2(/>) = TP 2 d = 3: /i3(/>) = ffl-p3 Let the quantity M(d, p) be the smallest possible covering of a set S with /id-balls of radius p m < p. The measure M(d) of <S is then given as the limit of M(d, p) as the radius of the balls approaches zero M(d) = lim j inf £ M , m ) j (B.3) Only one value of d results in M(d) taking on a non-zero, finite number. This is the Hausdorff-Besicovitch dimension D of the set S. For example, if S is a square area of unit dimensions, d = 1: hx{p) oc p. Thus M(l) oc p/p2 —> oo d = 2: fc2(p) « P2- T h u s M i 2 ) « /"V^2 -»• 1 d = 3: fes(p) « ps. Thus M(3) a ps/p2 0 The only non-vanishing, finite measure is for the ball h2, so that D = 2. This approach readily extends to arbitrary sets. For many of these, D is not an integer, and is greater than the topological dimension T. These sets are termed fractals, and D is called the fractal dimension. I l l An embedding set X is denned to be a Euclidean space that contains the fractal set S i.e., S C X. The dimension E of X provides an upper bound on D. Thus, D must always obey the double inequality T < D < E. The deterministic fractals are those that are constructed according to fixed, definite rules. The simplest such fractals are sets made up of the union of a finite number n G Z+ of compact sets [Hutc81] such that where the Si are similitudes, i.e., mappings composed of a translation, an or-thonormal transformation, and a homothety (a uniform scaling). The scaling factor r > 1 of the homothety describes the ratio of the set a to its subset 5,-(o). It is often referred to as the geometric ratio. The form of the set a is constrained to produce self-similarity, but may otherwise be arbitrarily chosen. The dimension of such a set can be easily ascertained when the intersection of the Si(a) has a smaller dimension than that of the set itself. (This condition is almost always the case [Mand82].) Given that a self-similar set a has a dimension D, it follows that each of the 5,- (a) must have the same dimension D, since this quantity is invariant under translation, scaling, and orthonormal transformation. Taking the measure of a to be the sum of the measures of the Si(a), this becomes B.3 Deterministic fractals a = S^a) U S2{a) U • • • U Sn(a), [T(|)]D ( P n r(i + fr r 112 Equating this with the direct measure of a, viz., M(£>) = lim(inf £ -Mll-L^l v ' p^o\pm<p ^ r ( i + f) m J leads to the condition The dimension of the set may therefore be expressed as D = log(n)/log(r). (B.4) It is important to distinguish clearly between fractal sets and self-similar sets, for neither is a strict subclass of the other. For example, a union of straight horizontal line segments connected at their endpoints forms a horizontal line, which is technically not a fractal, since D — T = 1. Fractals, on the other hand, are not necessarily characterized by self-similarity, for the similitudes 5,-are not the only mappings that produce them [Hutc8l]. In this work, attention is restricted to the class of objects that are both self-similar and fractal, as they provide a convenient domain containing all the properties of the general class of fractals. Example: Koch curve The Koch curve if is a simple self-similar curve, constructed in a recursive fashion. In the first stage of construction, the initial base is replaced by a gener-ator made up of four smaller sections (see figure B.l), each having a geometric ratio r = 3 with respect to the base. Each of these first-stage sections is then replaced in its turn by a scaled generator to obtain the second-stage figure. The Koch curve is defined to be the limit of the process as the number of stages approaches infinity. 113 base first stage second stage Figure B.l: Construction of Koch curve The resulting curve is self-similar in its embedding space (E = 2), with r = 3 and n = 4. Its dimension is therefore D = log(n)/log(r) = log(4)/log(3) » 1.26. In many respects, the Koch curve roughly approximates a coastline. Indeed, its length L increases as L{k) = C{n/r)k = C{l/r)k^-D\ where k is the level of the stage generated, and C is a constant depending on the size of the base. Denoting the length of the sections at stage k by the length may be written L{e) = Ce1-0, (B.5) 114 The similarity between eqs (B.l) and (B.5) is readily apparent. The example also shows that although the condition D > T implies that the curve is non-differentiable, it does not imply that continuity is necessarily lost. B.3.1 Parametric Representation An alternate characterization of self-similarity is to regard a fractal as an E-dimensional vector quantity a parametrized by a T-dimensional vector t. The parameterization process is illustrated for the case of a curve (T = 1) in a two-dimensional embedding space (E = 2). This process can be readily extended to the general case. A general class of self-similar curves can be constructed by generalizing the process used to form the Koch curve. An initial straight-line base is replaced by a generator composed of n equal-length sections. Apart from the requirement of contiguity of the sections, the form of the generator is arbitrary (figure B.2). Each section is in turn replaced by a scaled-down generator, the process being recursively continued until a limiting form is reached. Parametrization by a real-valued quantity t is done in a fashion which par-allels that of the construction process. To begin with, the location of the left and right endpoints of the initial section are left undisturbed by later stages of construction. They may therefore be unambiguously assigned correspondences to the parametric values t0 and tn respectively, where t0 < tn. Consider now the first stage of construction. The locations of the corners between the n line sections in the generator remain unaffected by later stages. They may therefore be assigned correspondences to the parametric values ti = t0 + (t'/n)[tl - t0] | i e {1 ,2 , (n - 1)}, 115 base Figure B.2: Construction of generalized curve the assignment being such that the values along the line form a strictly in-creasing sequence. This parameterization is continued in a similar way for all later stages of the construction. Each point on the curve therefore uniquely corresponds to a value of t. For an arbitrary point on such a parametrized curve, its location a(r) may be specified in two different ways. The first is with respect to the origin a{tQ) of the base section; the second is with respect to the starting point a(i,) of the generator section giving rise to that point (figure B.3). Comparing the two formulations, it follows that a(t) - a{t0) = a{U) - a(t0) + (l/r)iE(0,-)[a(to + n{t - tt)) - a{t0)} (B.6) for all t0 < t < tn. The i2(0,-) are rotation operators, that relate the orientation of the generator sections to that of the base. For the section running from to 116 a(*0) a(ii) a(rn) Figure B.3: Relation of descriptions of self-similar curve U+i, the rotation angle 0,- is given by 0 i = arctan(^4^4H) - arctan^'j " a ", 0 | ) . (B.7) - ax[ti) ax(tn) — ax[t0) For these curves, then, the similitudes 5,- are composed of a translation a(U) — a(t0), a rotation R(0i), and a uniform rescaling by a factor of r. The quantity n describes the scaling of the parameter t. It is referred to as the (parametric) scaling ratio. In general, this quantity is not uniquely denned, since a self-similar set with n = no is also self-similar with n — n 0 ; j £ Z+. To provide a unique characterization of this aspect of a fractal's behaviour, only the smallest value of n greater than unity is taken as the value of the scaling ratio. For convenience, the geometric ratio r is often expressed indirectly, using n and the similarity parameter H = log(r)/log(re). The similarity parameter describes the relation between the geometric ratio of the embedding-space and the scaling ratio. Comparison with eq.(B.4) shows that D = 1/H when l/E < H < 1/T. Although fully equivalent to the use ofl/D for deterministic fractals, the use of the similarity parameter H is more advantageous in the stochastic domain. Mandelbrot regards the quantity 1/H as the latent dimension of the 117 fractal, capturing its self-similar behaviour better in most respects than the Hausdorff-Besicovitch dimension D [Mand84]. For many purposes, then, H is the most appropriate measure of self-similarity. Generalizing from self-similar curves, the parametric equation for a self-similar set a may be written in the form - S(?o) = a{ti) - a{t0) + n-Hn{Qi)[a{t0 + n(t - £)) - a{t0)} (B.8) where n(Q,) is an orthonormal transformation, and 0 denotes its parameters. Note that it is the relative changes of a(7) that are translated, scaled, and transformed. This allows self-similarity to be well-defined even when a(?o) is a divergent quantity. B.3.2 Fractal Functions A special class of fractals are the fractal functions, comprising those fractals that are parametrized by an extrinsic co-ordinate system in the embedding space itself. Two different characterizations may be used to describe such functions. The first is the standard one, using the intrinsic parameter t. The second is the behaviour of the fractal in an embedding space containing t as one of the geometric dimensions. The intrinsic parameter t then becomes an extrinsic position vector u. Using this latter characterization, the function is therefore a generalized graph. For self-similar curves in two-dimensional space, u is a scalar, and the fractal functions become a linked pair, having the form ax(u) - ax(u0) = ax(ui) - ax(u0) (B.9) +n~H{[ax(u0 + n(u - «,•)) - ax(u0)] cos(0,) -[ay(u0 + n(u — Ui)) — ay(u0)] sin(0,-)} 118 ay(u) — ay(u0) — ay(ui) - ay(u0) (B.10) +n H{[ax(u0 + n(u - u,-)) - ax(u0)] sin(0,) + [ay(u0 + n(u — Ui)) - ay(u0)] cos(0,)} The general relation between the dimension D of these functions and H is not known. For a few scalar functions in two dimensions, however, it has been shown that D = 2 — H. It is conjectured that this relation holds in general, and that for functions embedded in three dimensions, D — 3 — H [Mand82]. Stochastic fractals are defined as sets of random variables that are self-similar with regard to their statistical properties, i.e., holds for their joint probability density functions. Using parametric represen-tation, this becomes a(f) - a(?0) = afi) - a(F0) + ^ ^(©^[a^o + n{t - t{)) - a(F0)] (B.ll) where the ij are the parametric values of the origin of the appropriate generator section. The 0t- are random variables specifying the parameters of the orthog-onal transformation II in the similitude Si. These fractals may be defined in a recursive fashion similar to that of the deterministic case, except that the generator is no longer a fixed pattern,'but is an ensemble of patterns. The ensemble determines the (joint) density functions of the 0,-. For the case of random curves through a two-dimensional embedding space, the stochastic counterpart to equation (B.6) is B.4 Stochastic fractals a = 5i(a) U 52(a) U • • • U 5n(a), a(r) - a(<0) = a(«,-) - a(*0) + ri ~HR{&i)[ai{t0 + n{t- ti)) - a(*0)], 119 where &i — arctan( ^ v{U+i) - a„(t,-) az(t,+i) - a.x(ti) ) — arctan( a y ( * n ) ~ ai/(*o) az(rn) - az(t0) •)• An instance of self-similar random sets can be constructed using a recursive procedure similar to that for the deterministic figure, except that each straight-line section present at a given stage is replaced by a (different) instance of the generator ensemble. Such a construction process is used in computer graphics to produce self-similar random curves; a corresponding process produces self-similar random surfaces (e.g., [FoFu82][HaBa84)]. B.4.1 Stationary Increments When the increments a(*2) — a(li) of a stochastic fractal are stationary (see appendix A), its description can be simplified in two ways. First, the transla-tional component of the similitude Si can be eliminated, since the probability densities of all increments must be of the same form. This implies that the translation a(t,) — a(io) of any sections relative to the base must be zero. Sec-—* ond, the distribution of transformation parameters 0,- must be the same for any section a(t,+i) — a(i<). If the generator sections have isotropic probability densities, any orthonormal transformation is compatible with the description. —# If the sections have anisotropic densities, however, 11(0,-) may be taken as the identity operator, for otherwise the sum of adjoining increments would have a density function of a form different from that of the original set. In general, then, stochastic fractals with stationary increments may be considered as having neither a translational nor a transformational component to their similitudes. Taken together, these two conditions imply that the stochastic properties of all generator sections are identical. This implies that only the first section need 120 be used to describe the fractal, so that the parametric value £,• may be set to to. Abolishing the translational and rotational components of the similitude in eq (B.ll) then leads to a(i) - a(?0) = n_H[a(?0 + n{t- t0)) - a(?o)]. This can be written in the more symmetrical form aft + n{t- to)) - a(?0) = nH[a(?0 + (* - ?o)) - a(i0)] which emphasizes that it is the behaviour of the increments that characterizes the fractal. In particular, note that stationarity of the increments does not imply stationarity of the fractal itself. A more general class of stochastic fractals is obtained by removal of the constraint that the scaling ratio n be an integer [MaNe68]. This takes advantage of the fact that for stochastic fractals with stationary increments, only the first generator section is required for their specification. The requirement of an integral number of sections may therefore be relaxed — the integral quantity n may be replaced by the real-valued quantity h > 1. If the fractal is self-similar for all parametric scaling factors, the scaling ratio h —* 1. By using the symmetry of fractals to expansion and contraction, the range of h may be extended to the positive real numbers, for the behaviour of a fractal with scaling ratio h is identical to one with scaling ratio 1/h. This yields a(?0 + h{t- to)) - a(?0) = hH[a(t0 + (?- t0)) - a(i0)] ; h > 0 as the general description of fractals with stationary increments. Since fractals with scaling ratios of h and 1/h are similar, however, the convention is made that h > 1. 121 Fractal functions Stationarity also leads to a simplification of the graphs of the fractal functions. For the case of the self-similar curve a(t) in a two-dimensional embedding space, the component graphs ax(u) and ay(u) become a pair of independent equations (cf. eqs (B.9) and (B.10)) 3Lx(U0 + h(u - u0) - az(u0) = hH[ax(u0 + (u - u0)) - az(u0)] (B-12) ay(u0 + M u ~~ uo) - atf(u0) = hH[ay(u0 + (u - u0)) - ay(u0)} (B.13) since the translational components have been removed, and IT(0,) = 1 implies Oi — 0. Although independent, these equations do not necessarily describe a stationary curve — for example, the relative scales of the two curves could differ by some non-zero finite ratio. Example: Brownian motion Brownian motion B(r) is the motion undergone by a small particle as it is randomly bombarded by the atoms and molecules of the surrounding fluid. Given any set of time steps {£,•}, where ti = t0 + iAt | * E Z+, the probability distribution of the increments B(tt) — B(i,+i) is a stationary, zero-mean Gaussian distribution, with variance of A\U — U+i\ = A\At\, where A is some positive number. Rescaling the time steps by an arbitrary factor h > 0 leads to a similar distribution, with variance -A|/iAt|. Thus, B(*,- + hAt) - B{ti) = /i1/2[B(t,- + At) - B(t,)], 122 for arbitrary r,- and h. Thus, Brownian motion is self-similar, with a similarity parameter H = 1/2, and a scaling ratio h —* 1. The relation D = 1/H implies that B(t) is two-dimensional. Indeed, the curve is capable of completely filling regions in the plane. It is important to realize that the detail of structure existing at all scales in deterministic fractals also exists in stochastic ones. For example, straight lines cannot accurately interpolate B(t) from its values at time steps {£,}. Between any two points r,- and U+i, its stochastic behaviour is completely re-created at a smaller scale. B.4.2 Fractional Brownian Motion Brownian motion can be generalized to obtain a class of self-similar stochastic fractals - the fractional Brownian motions Bjy(r), defined by the conditions [MaNe68]: 1. Bjy(O) = b0, where b0 is an arbitrary vector 2. BH(t) - B H(0) = ^ { / . ^ [ ( t - a)'"1/' - {-s)*-V*]dB{s) where t > 0 and 0 < H < 1. This is a moving average of B(t), weighted by the factor {t - s)11-1'2. The increments AB#(r) = B^(r + Ar) — B#(t) are stationary and are char-acterized by [MaNe68]: 1. Bff(t + At) — Bjy(r) has a Gaussian distribution, since it is the sum of Gaussian random variables 123 2. The mean increment (BH(t + At) — BH(t)^ = 0, since it is the sum of zero-mean random variables. 3. The variance (|Bjy(£ + At) - BJJ(I)| 2) oc |At| 2 f f , since the weighting fac-tor has an exponent of H — 1/2, while dB(t) has a similarity parameter of 1/2. The square root of the variance is proportional to \At\H for all At. Therefore, for any value h, BH(t + hAt) - BH{t) = hH[BH{t + At) - BH(t)}. This shows that Bn{t) is self-similar, with similarity parameter H, and scaling ratio h —* 1. Fractional Brownian motion is therefore an appropriate general-ization of regular Brownian motion, since B(t) is BH(t) when H = 1/2. Since B#(r) is isotropic, all component functions [Bff(c)],- are of the same form, denoted here simply by BJJ(X). The change in argument shows that these components are functions of an extrinsic parameter. The function B# (x) is also a fractal, with dimension D — 2 — H [Mand82]. Adjacent increments of BJJ(X) have a correlation E{[BH{x) - BH{x - 6)][BH{x + 6)- BH(x)}} = \ E{[BH(x + 6)- BH(-x - 6)12} - \ E{[BH(6) - BH(-6)}>} -E{[BH(x + 6)-BH(-6)}*} = cH/2[\2x + 26\2H + (26)2H - 2\x + 26\2], where 8 is the interval of the increments, and CH is some positive factor. The sign of the correlations depends only on the value of H [MaNe68]. For H — 1/2 (Brownian motion), the increments are uncorrected — the motion 124 is completely random, the past having no effect on the future. When H > 1/2, however, the correlations between increments are always positive, so that persistence occurs between the values of successive increments. As H —• 1, the function becomes smoothly varying. For H < 1/2, the correlations are negative, giving rise to antipersistence, with successive increments alternating between positive and negative values. Although the function B# (x) is nonstationary, its power spectrum can be calculated. It has the form [MaNe68] s{k)=v„ i^ r1-2*, where VH is a constant, parametrized by H. The power spectrum therefore has the same self-similarity properties as its corresponding graph. B.4.3 Fractional Gaussian Noise Since the graph B/r(x) is non-differentiable, its derivative B'# (x) does not exist in a strict mathematical sense. However, B'#(x) can be represented as a random Schwarz distribution: the limit of the derivative of a smoothed B#(x) as the amount of smoothing goes to zero. The resultant process is referred to as a fractional Gaussian noise [MaNe68], a stationary stochastic process of infinite variance. Fractional Gaussian noise is zero-mean, with a correlation function R(r) = r*|r | 2 "- 2 , and a power spectrum S(k) = WHlk]1-™, 125 where rH and WJJ are positive quantities parametrized by H, and 1/2 < H < 1 [MaNe68]. The lower bound on H prevents divergence of S(k) as A; —• oo. The form of the power spectrum is consistent with that of the power spectrum for fractional Brownian motion, since differentiation in the spatial domain corre-sponds to multiplication by +ik in the frequency domain. When the parameter H is formally replaced by H + 1, it is seen that frac-tional Brownian motion and fractional Gaussian noise both belong to a gener-alized class of functions T5+H(t), for which H is a non-zero quantity such that — 1/2 < H < 1. The fractional Brownian motions are true fractals; the frac-tional Gaussian noises are not. However, the spectral behaviour of all functions in this class is identical — a self-similarity of form over all possible scales. 126 Appendix C Technical Considerations The following sections are concerned with various technical aspects of the psy-chophysical experiments described in chapter 4. In particular, they discuss issues involved with the creation and display of the textures used. C . l Discretization of Power Spectra In practice, any physical realization of an image must be quantized, bounded, and discrete. The effect of quantization on perceived texture is small when the number of grey levels involved exceeds 16 [CaHii84]. For the textures used in the experiments of chapter 4, the standard deviation was set to 32 grey levels, and the textures were displayed using 256 grey levels. The effect of quantization is therefore considered negligible upon the perception of the textures displayed. The issues of boundedness and discretization, however, are more complex, and must be treated in greater detail. C . l . l Discrete Fourier Transform Consider the discrete image fxy with spacing Ax and A„ in the x— and the y—directions respectively, and with bounds x = ±Tx/2, y = ±Ty/2. For conve-127 nience, the number of points TY,- = T,-/At- in direction i is assumed to be even and equal to 2n,-. The finite discrete Fourier transform fa is then defined as [JeWa68] ^ n x - l ^ = WW £ £ frA,.A,exp[-i2*{rk/Nt + sl/Ny)]. - ' V z i V y r = - n x s = - n y This transformation considers fxy = fr&x SAy to be a spatially periodic function with period Tt in direction i. The transform fa is itself discrete and periodic, with period Nx and Ny in the k— and /— directions respectively. The original image fxy may be recovered by the inverse transformation n x - l n , —1 / x y = Jl 12 hi exp[i27r(px/NxAx + qy/NyAy)]. Discrete transforms are analogous to continuous transforms in several ways — in particular, the correlation function Rxy of a random field is the discrete Fourier transform of its power spectrum Ski [ScSh75,ch4]. Note that Rxy de-scribed in this way is based on the assumption that the random field is periodic. When the displacement is much less than the size of the image, the error from the true value of the non-periodic Rxy is small. The discrete Fourier transform can be used to produce a random field fxy, since nx — 1 ny—1 f *v = JZ 12 mki Zkiexp[i27r(px/NXAX +qy/NyAy)\, p=—nx q=—riy where the z w are an array of independent zero-mean, unit-variance complex Gaussian random variables, and rriki is a modulation function (see appendix 128 A). The power spectrum Ski of the generated field is given by |mfcj|2, and its (periodic) correlation function is given by Sxy. Owing to considerations of computational efficiency, the finite discrete Fourier transform (DFT) is often implemented as the fast Fourier transform (FFT). Apart from requiring the dimensions of the image to be integral powers of two, the performance of the FFT is no difference from that of the full DFT. Implementations of the FFT are robust, with little sensitivity to numerical error [Knut81,4.3.3]. C.1.2 Self-Similarity and Discrete Images The theorems on self-similarity developed in chapter 3 are applicable only to continuous random fields. Discretization and boundedness destroy true self-similarity, both at spatial scales less than the spacing A,- and greater than the period T,-. These effects are related, since discretization of a continuous function g(x) by a spacing A corresponds to the convolution of its transform g(k) by the translated functions g(k + ra/A), n £ Z [JeWa68,2.2]. A converse relation also holds for discretization in the spatial-frequency domain. The use of finite discrete representations therefore destroys self-similarity in both the spatial and frequency domains. However, if the value of g(x) is always small beyond the range T,-/2, and if g(k) has no significant values outside the Nyquist limit JVj/2T,-, the effect of discretization and boundedness on the shape of the functions is small. There-fore, when both R(x, y) and S(k, I) are effectively bandlimited, the random field generated by the finite discrete Fourier transform is effectively self-similar over 129 the corresponding range of intermediate scales. C.2 Generation of Textures Instances of a one-dimensional self-similar random field were generated by the Fourier transformation of an array of complex-valued random variables. The following subsections describe some of the technical details involved. C.2.1 Fourier Transformation A damping filter Dk was designed to minimize the ranges of both the correlation function Rx and the power spectrum Sk, while simultaneously keeping the shape of Sk effectively self-similar inside the Nyquist limit. It has the form Dk = 0 ; 0 < A: < /ci = ( e-(*-«x)»/» a _ 1 ) 2 ( e - ( * - K 2 ) > 2 _ 1)2 . K l < k < K 2 — 0 ; K2 < k = I)-* ; k < 0. The parameters Ki and K2 correspond to the lower and the upper cut-off fre-quencies of the filter. The parameter a governs the range of the edge of the filter. Between the bounds of Ki + a and K2 — a, Dk has a value approaching unity. Transforms were based on arrays of 8192 points. The value of a was set to 6 pixels, and «i and K2 were chosen so that the half-power points were 16 pixels and 4080 pixels. All transforms were found to exhibit smooth behaviour at the limits of their spatial range, and the values found there were generally several orders of magnitude smaller than those at the origin. The power spectra 130 and correlation functions of the textures used in the experiments were there-fore effectively bandlimited, so that Rx and Sk approximated their continuous counterparts R(x) and S(k). The target textures were produced from an array of 8192 independent Gaus-sian random variables zk, each with a mean of zero and a unit variance. The random variables were complex-valued, with random phases produced by gen-erating the real and imaginary components independently. Since the output image was real-valued, zk = — z*_k, so that only half of the array needed to be generated directly. Each of the zk was multiplied by a non-negative real-valued function mk. Fourier transformation of this array then created a random field with a power spectrum Sk = m\. The textures displayed were 256-pixel sections of the output of the FFT. Since these textures subtended a longitudinal angle of 4°, the half-power points of Dk corresponded to 0.12 cyc/deg and 31.9 cyc/deg. These values nicely bracket the limits of human spatial vision [CaRo68], so that the resulting tex-tures were effectively self-similar over all scales relevant to the human visual system. C.2.2 Random Number Generation The texture-generation algorithm outlined in the previous section involved the Fourier transformation of a field of independent Gaussian random variables. These quantities were generated via the polar method developed by Box, Muller, and Marsaglia [Knut81,3.4.1], which relies on two independent random variables that are both uniformly distributed between 0 and 1. The random variables that served to this procedure were obtained from the pseudonumber generator 131 random available on the Unix operating system. Since these random variables were produced by a finite-precision generator of pseudorandom numbers, it was necessary to use a series of statistical tests to check the quality of the numbers generated. The following tests, described in [NaBa66], were adapted slightly to fit the Gaussian case. They were applied to sequences of 8192 successively-generated numbers: frequency test: this checks the distribution of the values generated. Each test involved 200 sets of 40 numbers each, using 10 levels of quantization. serial test: this checks the degree of randomness between successive numbers. Each test involved 200 sets of 40 numbers each, using 10 levels of quanti-zation. run tests: these check the distribution of runs of values above and below the mean, as well as runs of steadily increasing and decreasing values. Each test involved 8192 numbers. correlation test: this checks the distribution of the correlation product of numbers separated by a given lag. Each test involved 200 sets of 40 numbers each; lags examined ranged from 1 to 25. Several hundred sets of numbers were tested against the hypothesis that they formed a set of true Gaussian random variables. The sets selected for use were those with the lowest x 2 values, or equivalently, those with the lowest probability p of being non-random. The sets chosen typically had p < 0.15 for the frequency and the serial tests, p < 0.10 for each of the various run tests, and p < 0.20 for the correlation test at each of the lags examined. 132 cd/m2 6 0 -4 0 -2 0 -X , X X X X X X X X' X X X' X/ x ,' x x x x x * J_ 5 0 1 0 0 1 5 0 2 0 0 g r e y l e v e l Figure C.l: calibration curve for monitor C.3 Monitor Calibration All textures used in the experiments of chapter 4 were displayed on a Hitachi HM-2719B-C-11 monitor. Calibration at the settings used was done using a spot photometer. Luminance of the screen was measured at 25 different grey levels ranging from 10 to 250 in steps of 10. The results are shown in figure C.l. A least-squares fit was made of the data for grey levels in the range 100—160. This range corresponded to the values within one standard deviation of the mean g — 128 used for the displays. The calibration equation obtained was L{g) = 0.660 - 54.9, (C.l) where L is the luminance in cd/m2, and g G {0,1,..., 255} is the grey level. 133 The curve is plotted on the graph of figure C.l. Since the mean grey level of the textures was set to 128, and the standard deviation to 32, it follows from eq (C.l) that the mean luminance of the display was Lmean = 0.66^^ - 54.9 = 30.0cd/m\ The contrast of the displayed textures was therefore a C = = 0.7. 134 Appendix D Values of V and Z for Threshold Textures Let Hi(k) be a filter that is applied to a set of one-dimensional textures with power spectrum S(k). Among the possible measures on the set of filtered images are the relative contrast (!Hf(k)S(k)dk\i/2 * V I S(k)dk J and the zero-crossing density _ (Ik2Hf(k)S(k)dk\1/2 * \ fHf{k)S{k)dk ) ' This last relation is taken from [Papo84,ll-4]. When a series of m different filters is applied in parallel, the V,- and Z, can be formed into the composite measures V — (Vi, V 2 , . . . , Vm) and Z = (Zx, Z 2 , . . . , Zm). These quantities may be used as the bases for a multiresolution representation of texture. —* —* This appendix contains the values of V and Z calculated for the reference classes H G {—1/2,0,1/2,1}, h —• 1 used in the first set of experiments de-scribed in chapter 4. Also calculated are the corresponding values for the tex-tures at the upper and lower discrimination thresholds determined for subject 135 A. These threshold values form the bases for the predictions made in section 5.2. All values calculated are based on the formulation of Hi(k) given by Wilson and Gelb [WiGe84], viz., Hi(k) = afl-1/2^! exp{-(7raifc)2} - f32a2 exp{-(7r<72fc)2} + P^z exp{-(7ra3A;)2}], where a = 1, and the values of the f3j and cry are given in Table 5.1. 136 h 1 —* measure = V channel H = -0.500 H = -0.720 H = -0.310 A 5.36 x 10-2 2.94 X lO - 2 8.58 x lO"2 B 3.57 x lO- 2 2.32 X lO"2 4.90 x lO"2 C 2.22 x lO - 2 1.58 x l O - 2 2.79 x lO"2 D 1.85 x lO - 2 1.43 x lO"2 2.17 x lO - 2 E 7.62 x l O - 3 6.81 x 10"3 7.89 x l O - 3 F 5.38 x 10"3 5.60 x 10"3 4.89 x l O - 3 h -+ 1 —* measure = Z channel H = -0.500 H = -0.720 H = -0.310 A 2.15 2.33 1.99 B 4.54 4.86 4.27 C 6.56 6.82 6.34 D 9.36 9.72 9.04 E 17.70 18.21 17.25 F 35.27 36.26 34.39 __ —* —* Table D.l: values of V and Z for h —• 1 textures 137 h -> 1 measure = V channel H= 0.000 H = -0.225 H= 0.175 A 1.48 x l O - 1 1.03 x 10"1 1.69 x 10_1 B 6.48 x 10"2 5.48 X 10~2 6.29 x 10"2 C 3.14 x 10~2 2.99 x 10~2 2.76 x 10"2 D 2.19 x 10"2 2.26 x 10~2 1.81 x lO - 2 E 6.47 x 10 - 3 7.77 x 10"3 4.74 x 10 - 3 F 3.23 x l O - 3 4.54 x l O - 3 2.10 x 10"3 h -* 1 —* measure = Z channel H= 0.000 H - -0.225 H= 0.175 A 1.72 1.92 1.57 B 3.83 4.15 3.58 C 5.99 6.24 5.79 D 8.54 8.90 8.26 E 16.50 17.04 16.07 F 32.94 34.00 32.10 Table D.l (continued) 138 h->l —* measure = V channel II = 0.500 H= 0.305 H = 0.675 A 1.68 x 10"1 1.73 x 10"1 1.58 x 10"1 B 4.51 x lO - 2 5.69 x lO"2 3.51 X lO"2 C 1.61 x lO - 2 2.31 x lO"2 1.11 X lO"2 D 9.41 x 10~3 1.45 x l O - 2 6.05 x 10~3 E 1.97 x 10 - 3 3.46 x l O - 3 1.12 x 10"3 F 6.95 x 10~4 1.40 x 10"4 3.52 x 10"4 h 1 —* measure = Z channel H = 0.500 H = 0.305 H= 0.675 A 1.27 1.45 1.12 B 3.12 3.40 2.87 C 5.42 5.64 5.21 D 7.74 8.05 7.46 E 15.26 15.75 14.79 F 30.46 31.46 29.42 Table D.l (continued) 139 h 1 —* measure = V channel H= 1.000 H= 0.940 H= 1.095 A 1.39 x 10_1 1.43 x 10"1 1.35 X 10"1 B 2.13 X lO"2 2.37 X 10~2 1.84 X 10~2 C 5.26 x 10"3 6.17 x 10"3 4.25 x l O - 3 D 2.49 x 10"3 3.03 x l O - 3 1.90 x l O - 3 E 3.74 x 10 - 4 4.74 x 10~4 2.74 x 10~4 F 1.01 x 10~4 1.29 x l O - 4 7.46 x 10~5 h 1 —* measure = Z channel H = 1.000 H = 0.940 H = 1.095 A 0.85 0.90 0.78 B 2.41 2.51 2.28 C 4.72 4.84 4.52 D 6.95 7.06 6.79 E 13.62 13.94 13.04 F 25.26 26.71 22.37 Table D.l (continued) 140 


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