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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.)

Enhancing texture descriptors by a neighborhood approach to the non-additive entropy

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Autor(es):
Florindo, Joao Batista [1] ; Assirati, Lucas [1] ; Bruno, Odemir Martinez [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Sao Carlos Inst Phys, Sci Comp Grp, BR-13560970 Sao Carlos, SP - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: DIGITAL SIGNAL PROCESSING; v. 44, p. 14-25, SEP 2015.
Citações Web of Science: 1
Resumo

This work proposes to enhance well-known descriptors of texture images by extracting such descriptors both directly from pixel intensities as well as from the local non-additive entropy of the image. The method can be divided into four steps. 1) The descriptors are computed for the original image according to what is described in the literature. 2) The image is transformed by computing the non-additive entropy at each pixel, considering its neighborhood. 3) Similarly to step 1, the descriptors are computed from the transformed image. 4) Descriptors from the original and transformed images are combined by means of a Karhunen-Loeve transform. Four texture descriptors widely used in the literature were considered: Gabor wavelets, Gray-Level Co-occurrence Matrix, Local Binary Patterns and Bouligand-Minkowski fractal descriptors. The proposal is assessed by comparing the performance of the descriptors alone and after combined with the non-additive entropy. The results demonstrate that the combination achieved the best results both in image retrieval and classification tasks. The entropy is still more efficient in local-based methods: Local Binary Patterns and Gray-Level Co-occurrence Matrix. (C) 2015 Elsevier Inc. 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: 13/22205-3 - Organização espacial de tecidos e análise fractal para a taxonomia automatizada de plantas
Beneficiário:Joao Batista Florindo
Linha de fomento: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado
Processo FAPESP: 12/19143-3 - Geometria fractal e análise de imagens aplicadas à Biologia Vegetal
Beneficiário:Joao Batista Florindo
Linha de fomento: Bolsas no Brasil - Pós-Doutorado