Segmenting Live Cattle using a New Approach to Combine Superpixels and SegNets - Gonçalves, Diogo Nunes; Gonçalves, Wesley Nunes; Gomes, Rodrigo da Costa; Araujo, Anderson Viçoso de; Pistori, Julia Gindri Bragato; Higa, Gabriel Toshio Hirokawa; Weber, Vanessa Aparecida de Moraes; Pistori, Hemerson (Anais do Workshop de Visão Computacional (WVC); 2023: Anais do XVIII Workshop de Visão ComputacionalDO - 10.5753/wvc.2023.27544, 2023)
A new strategy for cattle image segmentation is proposed by combining the strengths of SegNets and superpixel classification using CNNs. The individual strengths of these segmentation techniques can be seen as complementary. Thus, we investigate the combination of both through the following operators: MEAN, MULT, MAX, OR, and AND. This new approach is tested on a dataset containing 154 labeled images from cattle captured in a real livestock farm scenario, with complex poses and background. A pixelwise accuracy of 94.1% has being achieved by the proposed approach, which is higher than the original methods applied separately.