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Blur Parameter Identification Through Optimum-Path Forest

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Autor(es):
Pires, Rafael G. ; Fernandes, Silas E. N. ; Papa, Joao Paulo ; Felsberg, M ; Heyden, A ; Kruger, N
Número total de Autores: 6
Tipo de documento: Artigo Científico
Fonte: COMPUTER ANALYSIS OF IMAGES AND PATTERNS; v. 10425, p. 11-pg., 2017-01-01.
Resumo

Image acquisition processes usually add some level of noise and degradation, thus causing common problems in image restoration. The restoration process depends on the knowledge about the degradation parameters, which is critical for the image deblurring step. In order to deal with this issue, several approaches have been used in the literature, as well as techniques based on machine learning. In this paper, we presented an approach to identify blur parameters in images using the Optimum-Path Forest (OPF) classifier. Experiments demonstrated the efficiency and effectiveness of OPF when compared against some state-of-the-art pattern recognition techniques for blur parameter identification purpose, such as Support Vector Machines, Bayesian classifier and the k-nearest neighbors. (AU)

Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
Beneficiário:João Paulo Papa
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
Beneficiário:Alexandre Xavier Falcão
Modalidade de apoio: Auxílio à Pesquisa - Temático