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Entropy 2016, 18(11), 384; doi:10.3390/e18110384

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

College of Information and Communication Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin 150001, China
These authors contributed equally to this work.
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Academic Editor: Carlo Cattani
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)
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Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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