대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
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신경회로망과 다중스케일 Bayesian 영상 분할 기법을 이용한 결 분할

Texture segmentation using Neural Networks and multi-scale Bayesian image segmentation technique

  • Kim Tae-Hyung (Dept. of Electronics Eng., Pusan Univ.) ;
  • Eom Il-Kyu (Dept. of Information and Communication Eng., Miryang Univ.) ;
  • Kim Yoo-Shin (Dept. of Electronics Eng., Pusan Univ.)
  • 발행 : 2005.07.01
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 Bayesian 추정법과 신경회로망을 이용한 새로운 결 분할 방법을 제안한다 신경회로망의 입력으로는 다중스케일을 가지는 웨이블릿 계수와 인접한 이웃 웨이블릿 계수들의 문맥정보를 사용하고, 신경회로망의 출력을 사후 확률로 모델링한다. 문맥정보는 HMT(Hidden Markov Tree) 모델을 이용하여 구한다. 제안 방법은 HMT를 이용한 ML(Maximum Likelihood) 분할 보다 더 우수한 결과를 보여준다. 또한 HMT를 이용한 결 분할 방법과 제안 방법을 이용한 결 분할 각각에 HMTseg라고 불리는 다중 스케일 Bayesian 영상 분할 기술을 이용하여 후처리를 행한 결 분할 또한 제안 방법이 우수함을 보여준다.

This paper proposes novel texture segmentation method using Bayesian estimation method and neural networks. We use multi-scale wavelet coefficients and the context information of neighboring wavelets coefficients as the input of networks. The output of neural networks is modeled as a posterior probability. The context information is obtained by HMT(Hidden Markov Tree) model. This proposed segmentation method shows better performance than ML(Maximum Likelihood) segmentation using HMT model. And post-processed texture segmentation results as using multi-scale Bayesian image segmentation technique called HMTseg in each segmentation by HMT and the proposed method also show that the proposed method is superior to the method using HMT.

키워드

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