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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15910
Title: Rotation-invariant texture recognition
Authors: Montoya Zegarra, Javier
Paulo Papa, Joao
Leite, Neucimar
da Silva Torres, Ricardo
Falcao, Alexandre
Keywords: Classification (of information);Data structures;Image analysis;Image descriptor;Optimum Path Forest;Steerable Pyramid Decomposition;Texture classification system;Pattern recognition
Issue Date: 2007
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-38149038001&partnerID=40&md5=fba3a1481de3f71fda7b80604208c844
Abstract: This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. 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 dataset. High classification rates demonstrate the superiority of the proposed method. © Springer-Verlag Berlin Heidelberg 2007.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15910
ISBN: urn:isbn:9783540768555
ISSN: 3029743
Appears in Collections:Artículos - Ciencia de la computación

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