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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/09.09.16.33
%2 sid.inpe.br/sibgrapi/2017/09.09.16.33.41
%T A Review of Texture Classification Methods and Databases
%D 2017
%A Cavalin, Paulo,
%A Oliveira, Luiz S.,
%@affiliation IBM Research
%@affiliation Universidade Federal do Paraná - UFPR
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%8 Oct. 17-20, 2017
%S Proceedings
%I Sociedade Brasileira de Computação
%J Porto Alegre
%K Texture recognition, Image recognition, Deep Learn- ing.
%X In this survey, we present a review of methods and resources for texture recognition, presenting the most common techniques that have been used in the recent decades, along with current tendencies. That said, this paper covers since the most traditional approaches, for instance texture descriptors such as gray-level co-occurence matrices (GLCM) and Local Binary Patterns (LBP), to more recent approaches such as Convolutional Neural Networks (CNN) and multi-scale patch-based recognition based on encoding approaches such as Fisher Vectors. In addition, we point out relevant references for benchmark datasets, which can help the reader develop and evaluate new methods.
%@language en
%3 sibgrapi_paper2017.pdf


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