Polarimetric Image Segmentation via Maximum-Likelihood Approximation and Efficient Multiphase Level-Sets

  • Authors:
  • Ismail Ben Ayed;Amar Mitiche;Ziad Belhadj

  • Affiliations:
  • IEEE;IEEE;IEEE

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2006

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Abstract

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.