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UBC Theses and Dissertations

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UBC Theses and Dissertations

Multi-resolution stereo vision with application to the automated measurement of logs Clark, James Joseph


A serial multi-resolution stereo matching algorithm is presented that is based on the Marr-Poggio matcher (Marr and Poggio, 1979). It is shown that the Marr-Poggio feature disambiguation and in-range/out-of-range mechanisms are unreliable for non-constant disparity functions. It is proposed that a disparity function estimate reconstructed from the disparity samples at the lower resolution levels be used to disambiguate possible matches at the high resolutions. Also presented is a disparity scanning algorithm with a similar control structure, which is based on an algorithm recently proposed by Grimson (1985). It is seen that the proposed algorithms will function reliably only if the disparity measurements are accurate and if the reconstruction process is accurate. The various sources of errors in the matching are analyzed in detail. Witkin's (Witkin, 1983) scale space is used as an analytic tool for describing a hitherto unreported form of disparity error, that caused by spatial filtering of the images with non-constant disparity functions. The reconstruction process is analyzed in detail. Current methods for performing the reconstruction are reviewed. A new method for reconstructing functions from arbitrarily distributed samples based on applying coordinate transformations to the sampled function is presented. The error due to the reconstruction process is analyzed, and a general formula for the error as a function of the function spectra, sample distribution and reconstruction filter impulse response is derived. Experimental studies are presented which show how the matching algorithms perform with surfaces of varying bandwidths, and with additive image noise. It is proposed that matching of scale space feature maps can eliminate many of the problems that the Marr-Poggio type of matchers have. A method for matching scale space maps which operates in the domain of linear disparity functions is presented. This algorithm is used to experimentally verify the effect of spatial filtering on the disparity measurements for non-constant disparity functions. It is shown that measurements can be made on the binocular scale space maps that give an independent estimate of the disparity gradient this leads to the concept of binocular diffrequency. It is shown that the diffrequency measurements are not affected by the spatial filtering effect for linear disparities. Experiments are described which show that the disparity gradient can be obtained by diffrequency measurement. An industrial application for stereo vision is described. The application is automated measurement of logs, or log scaling. A moment based method for estimating the log volume from the segmented two dimensional disparity map of the log scene is described. Experiments are described which indicate that log volumes can be estimated to within 10%.

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