Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
532749 | Pattern Recognition | 2009 | 14 Pages |
Abstract
This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has an efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, curvature, Zernike moments and multiscale fractal dimension).
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
André Ricardo Backes, Dalcimar Casanova, Odemir Martinez Bruno,