Texture image segmentation using Vonn mixtures-based hidden Markov tree model and relative phase

2020 ◽  
Vol 79 (39-40) ◽  
pp. 29799-29824
Author(s):  
Pan-pan Niu ◽  
Li Wang ◽  
Xin Shen ◽  
Qian Wang ◽  
Xiang-yang Wang
2009 ◽  
Author(s):  
Fang Liu ◽  
Kai Yang ◽  
Hongxia Hao ◽  
Biao Hou ◽  
Hua Zhong

2015 ◽  
Vol 29 ◽  
pp. 138-152 ◽  
Author(s):  
Xiang-yang Wang ◽  
Wei-wei Sun ◽  
Zhi-fang Wu ◽  
Hong-ying Yang ◽  
Qin-yan Wang

2013 ◽  
Vol 303-306 ◽  
pp. 1105-1108
Author(s):  
Yin Hui Zhang ◽  
Zi Fen He ◽  
Sen Wang ◽  
Zhong Hai Shi

Image segmentation methods that exploit multiscale information about images to be estimated have been extensively studied, typically using the Hidden Markov Tree (HMT) framework. we incorporate wavelet coefficients information of the original image in the form of Hidden Markov Tree model prior for the object segmentation. In this paper, we derive a generalized closed form inference scheme to exact determine the posterior likelihood at each iteration with definite number of iteration steps. Extensive experiments show that this method performs better than many competitive multiscale image segmentation methods.


2009 ◽  
Vol 28 (2) ◽  
pp. 156-160 ◽  
Author(s):  
Biao HOU ◽  
Feng LIU ◽  
Li-Cheng JIAO ◽  
Hui-Dong BAO

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