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Optimal Grouping of Line Segments into Convex Sets.
Author(s) -
Bahram Parvin,
Suresh Viswanathan
Publication year - 1995
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.9.63
Subject(s) - line segment , regular polygon , line (geometry) , novelty , convexity , enhanced data rates for gsm evolution , computer science , artificial intelligence , domain (mathematical analysis) , mathematics , canny edge detector , algorithm , pattern recognition (psychology) , edge detection , image (mathematics) , image processing , geometry , mathematical analysis , philosophy , theology , financial economics , economics
In this paper, we present a technique for grouping line segments into convex sets, where the line segments are obtained by linking edges obtained from the Canny edge detector. The novelty of the approach is twofold: first we define an efficient approach for testing the global convexity criterion, and second, we develop an optimal search based on dynamic programming for grouping the line segments into convex sets. We show results on real images, and present a specific domain where this type of grouping can be directly applied.

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