A complex network-based approach for boundary shape analysis

Autor(es)

Backes, Andre Ricardo ^[1] ; Casanova, Dalcimar ^[1] ; Bruno, Odemir Martinez ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION; v. 42, n. 1, p. 54-67, JAN 2009.
• Citações Web of Science: 66

Resumo

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)

• Processo FAPESP: 06/53972-6 - Análise e identificação de espécies vegetais através de textura
• Beneficiário: Dalcimar Casanova
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 06/54367-9 - Estudos de metodos de analise de complexidade em imagens.
• Beneficiário: André Ricardo Backes
• Modalidade de apoio: Bolsas no Brasil - Doutorado