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|Type:||Artigo de periódico|
|Title:||Learning how to extract rotation-invariant and scale-invariant features from texture images|
|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.|
|Citation:||Eurasip Journal On Advances In Signal Processing. Springer, 2008.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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