Controlling the Search for Convex Groups

This paper describes an efficient algorithm for the perceptual grouping of line segments. The method uses a geometry-based measure of affinity between pairs of lines to guide group formation, and implements a search control procedure that is intended to reduce search complexity when image characteristics lead to a combinatorially large number of possible groups. We also present a ranking system that identifies the polygons that offer the most plausible explanation for the observed image data. The method is applied in the context of finding convex groups, and is experimentally shown to outperform existing algorithms, particularly in images with significant clutter, strong texture, and long, curved contours.

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