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UBC Theses and Dissertations

Perceptual organization and symmetry in visual object recognition Wilson, Susan E.


A system has been implemented which is able to detect symmetrical groupings in edge images. The initial stages of the algorithm consist of edge detection, curve smoothing, and the extension of the perceptual grouping phase of the SCERPO [Low87] vision system to enable detection of instances of endpoint proximity and curvilinearity among curved segments. The symmetry detection stage begins by first locating points along object boundaries which are significant in terms of curvature. These key points are then tested against each other in order to detect locally symmetric pairs. An iterative grouping procedure is then applied which matches these pairs together using a more global definition of symmetry. The end result of this process is a set of pairs of key points along the boundary of an object which are bilaterally symmetric, along with the axis of symmetry for the object or sub-object. This paper describes the implementation of this system and presents several examples of the results obtained using real images. The output of the system is intended for use as indexing features in a model-based object recognition system, such as SCERPO, which requires as input a set of spatial correspondences between image features and model features.

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