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

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3 Author(s)
Ben Ayed, I. ; Inst. Nat. de la Rescherche Sci., Montreal, Que. ; Mitiche, A. ; Belhadj, Z.

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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:28 ,  Issue: 9 )