Human subjects easily perceive and extensively use shape regularities such as symmetry or periodicity when they are confronted with tile task of object description and recognition. A computer vision algorithm is presented that emulates such behavior in that it similarly makes use of shape redundancies for the concise description and meaningful segmentation of planar object contours. The contours are analyzed in so-called arc length space. This parameter space facilitates the detection of regularities under a broad range of viewing conditions. Several of the irregularities which have traditionally been treated in isolation, are given a unified substrate for their detection and use in building compact models. Regularity consistency checks are made and, if necessary, altered versions are inferred.
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