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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15908
Title: Learning how to extract rotation-invariant and scale-invariant features from texture images
Authors: Montoya Zegarra, Javier
Paulo Papa, Joao
Leite, Neucimar
da Silva Torres, Ricardo
Falcao, Alexandre
Keywords: Classification (of information);Computer networks;Image enhancement;Rotation;Textures;Brodatz;Classification rates;Data sets;Discriminating power;Distorted images;Feature vector (FV);image descriptor;Invariant features;multiclass recognition;rotation invariant;Steerable pyramid (SP);Texture features;texture images;Texture recognition;Feature extraction
Issue Date: 2008
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-45749084524&doi=10.1155%2f2008%2f691924&partnerID=40&md5=94ff5c1565eccb6cc9b0d2400d9cfaa2
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.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15908
ISSN: 16876172
Appears in Collections:Artículos - Ciencia de la computación

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