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Open AccessArticle

Texture Segmentation Using Laplace Distribution-Based Wavelet-Domain Hidden Markov Tree Models

by Yulong Qiao *,† and Ganchao Zhao
College of Information and Communication Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin 150001, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Carlo Cattani
Entropy 2016, 18(11), 384; https://doi.org/10.3390/e18110384
Received: 24 May 2016 / Revised: 20 October 2016 / Accepted: 21 October 2016 / Published: 4 November 2016
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory II)
Multiresolution models such as the wavelet-domain hidden Markov tree (HMT) model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model based on the two-state, zero-mean Laplace mixture model (LMM), the LMM-HMT, which provides significantly potential for characterizing real-world textures. By using the HMT segmentation framework, we develop LMM-HMT based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the introduced model and segmentation methods. View Full-Text
Keywords: wavelet-domain hidden Markov tree; Laplace distribution; texture segmentation; dynamic texture wavelet-domain hidden Markov tree; Laplace distribution; texture segmentation; dynamic texture
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Qiao, Y.; Zhao, G. Texture Segmentation Using Laplace Distribution-Based Wavelet-Domain Hidden Markov Tree Models. Entropy 2016, 18, 384.

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