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PAMI
2006

Polarimetric Image Segmentation via Maximum-Likelihood Approximation and Efficient Multiphase Level-Sets

9 years 2 months ago
Polarimetric Image Segmentation via Maximum-Likelihood Approximation and Efficient Multiphase Level-Sets
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.
Ismail Ben Ayed, Amar Mitiche, Ziad Belhadj
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PAMI
Authors Ismail Ben Ayed, Amar Mitiche, Ziad Belhadj
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