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.
Ben Ayed
Ismail
I
Institut National de la Recherche Scientifique, INRS-EMT 800, de La Gauchetière Ouest, Montréal, QC, H5A 1K6, Canada. benayedi@emt.inrs.ca
Mitiche
Amar
A
Belhadj
Ziad
Z
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016013:Likelihood Functions; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012031:Refractometry / Q000379:methods*