@InProceedings{FreitasCordMaca:2020:FoSeCl,
               author = "Freitas, Charles N. C. and Cordeiro, Filipe R. and Macario, 
                         Valmir",
          affiliation = "{Universidade Federal Rural de Pernambuco} and {Universidade 
                         Federal Rural de Pernambuco} and {Universidade Federal Rural de 
                         Pernambuco}",
                title = "MyFood: A Food Segmentation and Classification System to Aid 
                         Nutritional Monitoring",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "nutrition, food, segmentation.",
             abstract = "The absence of food monitoring has contributed significantly to 
                         the increase in the populations weight. Due to the lack of time 
                         and busy routines, most people do not control and record what is 
                         consumed in their diet. Some solutions have been proposed in 
                         computer vision to recognize food images, but few are specialized 
                         in nutritional monitoring. This work presents the development of 
                         an intelligent system that classifies and segments food presented 
                         in images to help the automatic monitoring of user diet and 
                         nutritional intake. This work shows a comparative study of 
                         state-of-the-art methods for image classification and 
                         segmentation, applied to food recognition. In our methodology, we 
                         compare the FCN, ENet, SegNet, DeepLabV3+, and Mask RCNN 
                         algorithms. We build a dataset composed of the most consumed 
                         Brazilian food types, containing nine classes and a total of 1250 
                         images. The models were evaluated using the following metrics: 
                         Intersection over Union, Sensitivity, Specificity, Balanced 
                         Precision, and Positive Predefined Value. We also propose a system 
                         integrated into a mobile application that automatically recognizes 
                         and estimates the nutrients in a meal, assisting people with 
                         better nutritional monitoring. The proposed solution showed better 
                         results than the existing ones in the market. The dataset is 
                         publicly available at the following link 
                         http://doi.org/10.5281/zenodo.4041488.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00039",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00039",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43B8HEB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43B8HEB",
           targetfile = "Paper_ID_63_camara_ready_version_v2.pdf",
        urlaccessdate = "2025, Jan. 25"
}