@InProceedings{BehaineIde:2021:EgImNo,
               author = "Behaine, Carlos Alberto Ramirez and Ide, Jaime S",
          affiliation = "University of Passo Fundo, Brazil  and Yale University, USA",
                title = "An egg image noise model for digital visual counting processing",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "image noise models, visual counting processing.",
             abstract = "Contactless counting is a suitable technique for the measurement 
                         of fragile commodities, acting as a successful tool for industrial 
                         production control. Visual counting processing is one of the most 
                         common contactless methods for non-invasive measurements. However, 
                         the creation of accurate models for processing images in realistic 
                         scenarios is still challenging due to the existence of noise in 
                         optical sensors. This paper proposes an egg image noise model for 
                         digital visual counting processing that incorporates particular 
                         aspects of real images in such acquisition systems. The matching 
                         function is defined in hue saturation value (HSV) color space, and 
                         a classical nearest neighbor cluster classification is utilized 
                         for the counting. Validation experiments are executed with low and 
                         high diversity test images, and the performance of the proposed 
                         model is compared to existing methods. The matching function 
                         results suggest that the introduced egg image noise model is able 
                         to represent more accurately complex aspects of egg images in an 
                         industrial environment. The comparative results show that the 
                         proposed model significantly improves digital visual counting, in 
                         terms of egg counting errors, and outperforms in 9% the second 
                         best method.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00047",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00047",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CALPL",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CALPL",
           targetfile = "Paper ID 8.pdf",
        urlaccessdate = "2024, May 06"
}