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A multiscale edge detection algorithm based on wavelet domain vector hidden Markov tree model

A multiscale edge detection algorithm based on wavelet domain vector hidden Markov tree model,10.1016/j.patcog.2003.11.006,Pattern Recognition,Junxi S

A multiscale edge detection algorithm based on wavelet domain vector hidden Markov tree model   (Citations: 15)
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Abstract The wavelet analysis is an e5cient tool for the detection of image edges. Based on the wavelet analysis, we present an unsupervised learning algorithm to detect image edges in this paper. A wavelet domain vector hidden Markov tree (WD-VHMT) is employed in our algorithm to model the statistical properties of multiscale and multidirectional (subband) wavelet coe5cients of an image. With this model, each wavelet coe5cient is viewed as an observation of its hidden state and the hidden state indicates if the wavelet coe5cient belongs to an edge. The WD-VHMT model can be learned by an expectation–maximization algorithm. After the model is learned, we employ an extended Viterbi algorithm to uncover the hidden state sequences according to the maximum,a posterior estimation. The experiment results of the edge detection for several images are provided to evaluate our algorithm. ? 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. Keywords: Edge detection; Hidden Markov tree (HMT) models; Expectation–maximization (EM); Wavelets
Journal: Pattern Recognition - PR , vol. 37, no. 7, pp. 1315-1324, 2004
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