| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, Sao Carlos, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | PATTERN RECOGNITION; v. 42, n. 1, p. 54-67, JAN 2009. |
| Web of Science Citations: | 66 |
| 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 all 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). (C) 2008 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 06/53972-6 - Analysis and identification of plant species through texture |
| Grantee: | Dalcimar Casanova |
| Support Opportunities: | Scholarships in Brazil - Master |