M. S. Nery, A. M. Machado, Mario Fernando Montenegro Campos, Flávio L. C. Pádua, Rodrigo L. Carceroni, José P. Queiroz-Neto.
We present a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task, we propose a general set of features and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a "black box" and focus our research in the determination of which input information must bring a robust fish discrimination. All the experiments were performed with fish species of Rio Grande river in Minas Gerais, Brazil. This work has been developed as part of a wider research , which has as main goal the development of effective fish ladders for the Brazilian dams.
http://doi.ieeecomputersociety.org/10.1109/SIBGRAPI.2005.25Caso o link acima esteja inválido, faça uma busca pelo texto completo na Web: Buscar na Web