Image Thresholding Improved by Global Optimization Methods

Autor(es)

Balabanian, Felipe ; Sant'Ana da Silva, Eduardo ; Pedrini, Helio

• 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