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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.)

Improved texture image classification through the use of a corrosion-inspired cellular automaton

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Autor(es):
da Silva, Nubia Rosa [1, 2] ; Van der Weeen, Pieter [3] ; De Baets, Bernard [3] ; Bruno, Odemir Martinez [1, 2]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Sao Carlos Inst Phys, Sci Comp Grp, BR-13560970 Sao Carlos, SP - Brazil
[3] Univ Ghent, Dept Math Modelling Stat & Bioinformat, B-9000 Ghent - Belgium
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: Neurocomputing; v. 149, n. C, p. 1560-1572, FEB 3 2015.
Citações Web of Science: 5
Resumo

In this paper, the problem of classifying synthetic and natural texture images is addressed. To tackle this problem, an innovative method is proposed that combines concepts from corrosion modeling and cellular automata to generate a texture descriptor. The core processes of metal (pitting) corrosion are identified and applied to texture images by incorporating the basic mechanisms of corrosion in the transition function of the cellular automaton. The surface morphology of the image is analyzed before and during the application of the transition function of the cellular automaton. In each iteration the cumulative mass of corroded product is obtained to construct each of the attributes of the texture descriptor. In the final step, this texture descriptor is used for image classification by applying Linear Discriminant Analysis. The method was tested on the well-known Brodatz and Vistex databases. In addition, in order to verify the robustness of the method, its invariance to noise and rotation was tested. To that end, different variants of the original two databases were obtained through addition of noise to and rotation of the images. The results showed that the proposed texture descriptor is effective for texture classification according to the high success rates obtained in all cases. This indicates the potential of employing methods taking inspiration from natural phenomena in other fields. (C) 2014 Elsevier B.V. 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: 11/21467-9 - Reconhecimento de padrões heterogêneos e suas aplicações em biologia e nanotecnologia
Beneficiário:Núbia Rosa da Silva
Linha de fomento: Bolsas no Brasil - Doutorado