| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 95 === A multiscale texture image segmentation approach based on the
contextual hidden Markov tree (CHMT) model and boundary refinement is
proposed. The hidden Markov tree models (HMT) is a statistical model of tree
structure for capturing properties of wavelet coefficients. The HMT model
describes persistence property of wavelet coefficients, but loses clustering
property. We have proposed the CHMT model which improved from the HMT
model by enhancing the clustering property.
The CHMT model reinforces clustering property by using extended
coefficients without changing the wavelet tree structure; thus the HMT
training scheme can be easily modified to estimate the parameters of the
CHMT model.
In this study, the CHMT model is applied for texture segmentation. For
each texture, we use the CHMT model to train a set of parameters and then
utilize these parameters compute likelihood functions for all mulitscale
squares of a test image. At last, we segment the test image with the principle
of maximum likelihood. Only based on the CHMT model, the segmentation
results are not good enough when the size of dyadic square is small; thus the
boundary refinement algorithm is adopted to fuse the multiscale square to get
better-quality segmented results. The segmented results based on the HMT
and CHMT models are compared to show the improvement of the CHMT
model over the HMT model; moreover, the boundary refinement algorithm is
also evaluated to show its ability.
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