@InProceedings{FerrazBorCavGonSai:2018:EvCoNe,
               author = "Ferraz, Carolina Toledo and Borges, Tamiris T. N. and Cavichiolli, 
                         Adriane and Gonzaga, Adilson and Saito, Jos{\'e} H.",
          affiliation = "UNIFACCAMP and {Federal Institute of S{\~a}o Paulo} and 
                         {University of S{\~a}o Paulo} and {University of S{\~a}o Paulo} 
                         and UNIFACCAMP",
                title = "Evaluation of convolutional neural networks for raw food texture 
                         classification under variations of lighting conditions",
            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 = "texture classification, CNN, light intensity.",
             abstract = "This work is a preliminary evaluation of convolutional neural 
                         networks (CNN) applied to food texture classification, 
                         particularly when the texture is subject to changes in the 
                         lighting conditions. Four previously published CNN architectures 
                         (Alexnet, Resnet 18, Resnet 34 and Resnet 50) are investigated and 
                         compared to local descriptors designed specifically for this task. 
                         Although preliminary results indicate that the investigated CNN 
                         are outperformed by the descriptors, further analysis are required 
                         to investigate the impact of the experimental design adopted in 
                         this work-in-progress; especially in regard to the number of 
                         training samples and CNN configuration.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S48B3H",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3S48B3H",
           targetfile = "sibgrapi_2018_versaofinal.pdf",
        urlaccessdate = "2024, May 03"
}