Shape classification using complex network and Multi-scale Fractal Dimension

Autor(es)

Backes, Andre Ricardo ^[1] ; Bruno, Odemir Martinez ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, BR-13560970 Sao Paulo - Brazil

^[2] Univ Sao Paulo, Inst Fis Sao Carlos, BR-13560970 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION LETTERS; v. 31, n. 1, p. 44-51, JAN 1 2010.
• Citações Web of Science: 39

Resumo

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V. (AU)

• Processo FAPESP: 06/54367-9 - Estudos de métodos de análise de complexidade em imagens
• Beneficiário: André Ricardo Backes
• Linha de fomento: Bolsas no Brasil - Doutorado