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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Image Thresholding Improved by Global Optimization Methods

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Autor(es):
Balabanian, Felipe ; Sant'Ana da Silva, Eduardo ; Pedrini, Helio
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: APPLIED ARTIFICIAL INTELLIGENCE; v. 31, n. 3, p. 197-208, 2017.
Citações Web of Science: 1
Resumo

Image thresholding is a common segmentation technique with applications in various fields, such as computer vision, pattern recognition, microscopy, remote sensing, and biology. The selection of threshold values for segmenting pixels into foreground and background regions is usually based on subjective assumptions or user judgments under empirical rules or manually determined. This work describes and evaluates six effective threshold selection strategies for image segmentation based on global optimization methods: genetic algorithms, particle swarm, simulated annealing, and pattern search. Experiments are conducted on several images to demonstrate the effectiveness of the proposed methodology. (AU)

Processo FAPESP: 15/12228-1 - Detecção e Reconhecimento de Eventos Complexos em Vídeos
Beneficiário:Hélio Pedrini
Linha de fomento: Bolsas no Exterior - Pesquisa