Using down-sampling for multiscale analysis of texture images

Autor(es)

Silva, Pedro M. ^{[1, 2]} ; Florindo, Joao B. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Camp Nas, Inst Math Stat & Sci Comp, Rua Sergio Buarque Holanda 651, BR-13083859 Campinas, SP - Brazil

^[2] Fed Inst Educ Sci & Technol Espirito Santo, Rodovia Governador Jose Sete 184, BR-29150410 Cariacica, ES - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION LETTERS; v. 125, p. 411-417, JUL 1 2019.
• Citações Web of Science: 0

Resumo

This work proposes the study of a simple yet efficient strategy to accomplish multiscale analysis of gray level images. The method is applied to the classification of texture/material images. The proposal employs histograms of down-sampled versions of the initial image to compose a feature vector. A statistical model based on Markov random fields is also developed and employed to explain how the information conveyed by the proposed descriptors can express degrees of homo/heterogeneity in the image. This is widely known to be a fundamental feature in texture analysis. The accuracy of our descriptors is compared to several state-of-the-art approaches and the achieved results confirm our expectation that a straightforward method can be efficiently employed even in the recognition of objects in large and complex databases. (C) 2019 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 16/16060-0 - Reconhecimento de Padrões em Imagens Baseado em Sistemas Complexos
• Beneficiário: Joao Batista Florindo
• Modalidade de apoio: Auxílio à Pesquisa - Regular