TY - JOUR
T1 - Robust and efficient detection of salient convex groups
JF - Pattern Analysis and Machine Intelligence, IEEE Transactions on
Y1 - 1996
A1 - Jacobs, David W.
KW - complexity;computer
KW - complexity;contours;image
KW - computational
KW - convex
KW - detection;feature
KW - detection;object
KW - extraction;object
KW - groups;computational
KW - organisation;proximity;salient
KW - recognition;
KW - recognition;line
KW - recognition;perceptual
KW - segment
KW - vision;edge
AB - This paper describes an algorithm that robustly locates salient 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 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 run time is O(n ^{2}log(n)+nm), 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
VL - 18
SN - 0162-8828
CP - 1
M3 - 10.1109/34.476008
ER -