Polarimetric Image Segmentation via Maximum-Likelihood Approximation and Efficient Multiphase Level-Sets
Issue No. 09 - September (2006 vol. 28)
Ismail Ben Ayed , IEEE
Amar Mitiche , IEEE
Ziad Belhadj , IEEE
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.
Polarimetric images, complex Wishart distribution, complex Gaussian distribution, level set active contour segmentation, maximum-likelihood approximation.
I. Ben Ayed, Z. Belhadj and A. Mitiche, "Polarimetric Image Segmentation via Maximum-Likelihood Approximation and Efficient Multiphase Level-Sets," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1493-1500, 2006.