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.

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Ismail Ben Ayed, Amar Mitiche, Ziad Belhadj
• Keywords: Complex Polarimetric Image, Images, PAMI 2006, Simple Partition Constraint