Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets.

Ismail Ben Ayed; Amar Mitiche; Ziad Belhadj

doi:10.1109/tpami.2006.191

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Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets.

Ismail Ben Ayed·Amar Mitiche·Ziad Belhadj

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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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Algorithms Artificial Intelligence Databases, Factual Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Likelihood Functions Models, Statistical Pattern Recognition, Automated Refractometry Journal Article Research Support, Non-U.S. Gov't

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Ismail Ben Ayed, Amar Mitiche, Ziad Belhadj. 2006. Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets.. https://doi.org/10.1109/tpami.2006.191

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