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Issue No.01 - January (1996 vol.18)
pp: 23-37
ABSTRACT
<p><b>Abstract</b>—This paper describes an algorithm that robustly locates <it>salient</it> convex collections of line segments in an image. The algorithm is guaranteed to find all convex sets of line segments in which the length of the gaps between segments is smaller than some fixed proportion of the total length of the lines. This enables the algorithm to find convex groups whose contours are partially occluded or missing due to noise. We give an expected case analysis of the algorithm's performance. This demonstrates that salient convexity is unlikely to occur at random, and hence is a strong clue that grouped line segments reflect underlying structure in the scene. We also show that our algorithm's run time is <it>O</it>(<it>n</it><super>2</super> log(<it>n</it>) + <it>nm</it>), when we wish to find the m most salient groups in an image with n line segments. We support this analysis with experiments on real data, and demonstrate the grouping system as part of a complete recognition system.</p>
INDEX TERMS
Grouping, perceptual organization, convexity, proximity, nonaccidental properties, robust, efficiency, recognition.
CITATION
David W. Jacobs, "Robust and Efficient Detection of Salient Convex Groups", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.18, no. 1, pp. 23-37, January 1996, doi:10.1109/34.476008
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