Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets.
IEEE Trans Pattern Anal Mach Intell
; 28(9): 1493-500, 2006 Sep.
Article
em En
| MEDLINE
| ID: mdl-16929734
ABSTRACT
This study investigates a level set method for complex polarimetric image segmentation. It consists of minimizing a functional containing an original observation term derived from maximum-likelihood approximation and a complex Wishart/Gaussian image representation and a classical boundary length prior. The minimization is carried out efficiently by a new multiphase method which embeds a simple partition constraint directly in curve evolution to guarantee a partition of the image domain from an arbitrary initial partition. Results are shown on both synthetic and real images. Quantitative performance evaluation and comparisons are also given.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Refratometria
/
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Interpretação de Imagem Assistida por Computador
/
Aumento da Imagem
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Ano de publicação:
2006
Tipo de documento:
Article