Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets
Issue No. 09 - September (2006 vol. 28)
I. Ben Ayed , Inst. Nat. de la Rescherche Sci., Montreal, Que.
A. Mitiche , Inst. Nat. de la Rescherche Sci., Montreal, Que.
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
Image segmentation, Level set, Gaussian distribution, Active contours, Synthetic aperture radar, Speckle, Robustness, Image representation, Minimization methods, Partitioning algorithms,maximum-likelihood approximation., Polarimetric images, complex Wishart distribution, complex Gaussian distribution, level set active contour segmentation
I. Ben Ayed, A. Mitiche, Z. Belhadj, "Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1493-1500, September 2006, doi:10.1109/TPAMI.2006.191