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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Texture descriptor based on partially self-avoiding deterministic walker on networks

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Autor(es):
Goncalves, Wesley Nunes [1] ; Backes, Andre Ricardo [2] ; Martinez, Alexandre Souto [3] ; Bruno, Odemir Martinez [1]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, IFSC, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Fed Uberlandia, Fac Comp, BR-38408100 Uberlandia, MG - Brazil
[3] Univ Sao Paulo, FFCLRP, BR-14040901 Ribeirao Preto, SP - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 39, n. 15, p. 11818-11829, NOV 1 2012.
Citações Web of Science: 11
Resumo

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 11/01523-1 - Métodos de visão computacional aplicados à identificação e análise de plantas
Beneficiário:Odemir Martinez Bruno
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 10/08614-0 - Análise de texturas estáticas e dinâmicas e suas aplicações em biologia e nanotecnologia
Beneficiário:Wesley Nunes Gonçalves
Linha de fomento: Bolsas no Brasil - Doutorado