Color Image Segmentation Using a Gaussian Mixture Model and a Mean Field Annealing EM Algorithm(Image Processing, Image Pattern Recognition)

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概要

• 論文の詳細を見る

• In this paper we present an unsupervised color image segmentation algorithm based on statistical models. We have adopted the Gaussian mixture model to represent the distribution of color feature vectors. A novel deterministic annealing EM arid mean field theory from statistical mechanics are used to compute the posterior probability distribution of each pixel and estimate the parameters of the Gaussian Mixture Model. We describe the noncontexture segmentation algorithm that uses a deterministic annealing approach and the contexture segmentation algorithm that uses the mean field theory The experimental results show that the deterministic annealing EM and mean field theory provide a global optimal solution for the maximum likelihood estimators and that these algorithms can efficiently segment the real image

• 社団法人電子情報通信学会の論文
• 2003-10-01

著者

• Park So-young

  Department Of Computer Science Engineering Korea University

• Park Jong-hyun

  Dept. Of Computer Science Chonbuk National Univ.

• Park S‐y

  Korea Univ. Seoul Kor

• CHO Wan-Hyun

  Dept. of Statistics, Chonnam National Univ.

• PARK Soon-Young

  Dept. of Electronic Engineering, Mokpo National Univ.

• Cho Wan-hyun

  Dept. Of Statistics Chonnam National Univ.

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