Measuring the Convexity of Shapes

Thursday, November 16, 2000 - 9:30am - 10:30am
Keller 3-180
Davi Geiger (New York University)
Many recognition tasks requires shape understanding. One of the most important measures of shapes is convexity. Psychophysics experiments show that the human visual system prefers convexity over symmetry to select figure from background. While we have today a good understanding of symmetries of shapes (e.g., skeletons), we have not yet devoted (much) attention to convexity. In particular, today shapes can be classified either as convex or not convex(concave), but the psychophysics experiments refer to shapes that are not perfectly convex.

We propose a continuous measure of convexity for shapes. We investigate a Markov random field model for extracting convexity (we can't leave home without them, or , there are things that science can't explain but for everything else there are MRFs.) In our approach convexity becomes an emergent property from a sum of local interactions (were each local term does not contain convexity). We analyse our approach and extensively experiment with it.

This is work in collaboration with Nava Rubin and Hsing-Kuo Pao.