Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/80822
Type: Artigo de periódico
Title: Learning how to extract rotation-invariant and scale-invariant features from texture images
Author: Montoya-Zegarra, JA
Papa, JP
Leite, NJ
Torres, RDS
Falcao, AX
Abstract: Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system. Copyright (c) 2008 Javier A. Montoya-Zegarra et al.
Country: EUA
Editor: Springer
Rights: aberto
Identifier DOI: 10.1155/2008/691924
Date Issue: 2008
Appears in Collections:Unicamp - Artigos e Outros Documentos

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