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@InProceedings{BorgesGonz:2018:LoTeDe,
               author = "Borges, Tamiris Trevisan Negri and Gonzaga, Adilson",
          affiliation = "{University of Sao Paulo - USP and Federal Institute of Sao Paulo 
                         - IFSP} and {University of Sao Paulo - USP}",
                title = "Local Texture Descriptors for Color Texture Classification Under 
                         Varying Illumination",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Color texture classification, local texture descriptors, varying 
                         illumination.",
             abstract = "Color texture classification under varying illumination remains a 
                         challenge in the field of computer vision, and it greatly relies 
                         on the efficiency of the feature descriptors. The aim of the 
                         thesis is to improve the classification of color texture acquired 
                         with varying illumination sources by improving the description 
                         power of feature descriptors. We propose three new color texture 
                         descriptors, namely: the Opponent Color Local Mapped Pattern 
                         (OCLMP), which combines a local methodology (LMP) with the 
                         opponent-colors theory; the Color Intensity Local Mapped Pattern 
                         (CILMP), which extracts color and texture information jointly, in 
                         a multi-resolution fashion and the Extended Color Local Mapped 
                         Pattern (ECLMP), which applies two operators to extract color and 
                         texture information jointly as well. As the proposed methods are 
                         based on the LMP algorithm, they are parametric functions. Finding 
                         the optimal set of parameters for the descriptor can be a 
                         cumbersome task. Therefore, this work adopts genetic algorithms to 
                         automatically adjust the parameters. The methods were assessed 
                         using two texture data sets acquired under varying illumination 
                         sources: RawFooT (Raw Food Texture Database), and the KTH-TIPS-2b 
                         (Textures under varying Illumination, Pose and Scale Database). 
                         The experimental results show that the proposed descriptors are 
                         more robust to variations to the illumination source than other 
                         methods found in the literature. The improvement on the accuracy 
                         was higher than 15% in the RawFoot data set, and higher than 4% in 
                         the KTH-TIPS-2b data set.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S3JPCH",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3S3JPCH",
           targetfile = "CameraReady_Tamiris.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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