Earticle

초록

영어

Edge detection plays an important role in digital image processing. Based on the non-decimated wavelet which is shift invariant, in this paper, we develop a new edge detection technique using Hidden Markov Chain (HMC) model. With this proposed model (NWHMC), each wavelet coefficient contains a hidden state, herein, we adopt Laplacian model and Gaussian model to represent the information of the state “big” and the state “small”. The model can be trained by EM algorithm, and then we employ Viterbi algorithm to reveal the hidden state of each coefficient according to MAP estimation. The detecting results of several images are provided to evaluate the algorithm. In addition, the algorithm can be applied to noisy images efficiently.

목차

Abstract
 1. Introduction
 2. Edge detection using HMC model based on non-decimated wavelet(NWHMC)
  2.1. Statistical model for individual wavelet coefficients
  2.2. Hidden Markov Chain considering the inter-scale dependence
  2.3. Training parameters and Searching for hidden states
 3. Experimental results and analysis
  3.1. Realization of our algorithm and parameter selection
  3.2. Comparisons with Canny algorithm and WD-VHMT
  3.3. Experiments of noisy images using our algorithm
 4. Conclusion
 5. References

저자

• Renqi Zhang [ Dept. of Electronic Engineering, The Chinese University of Hong Kong ]
• Wanli Ouyang [ Dept. of Electronic Engineering, The Chinese University of Hong Kong ]
• Wai-Kuen Cham [ Dept. of Electronic Engineering, The Chinese University of Hong Kong ]

참고문헌

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간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605