Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space-Reference-Cited by-同舟云学术

Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space

Published:2011-03 Issue:3 Volume:44 Page:503-518
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Han Shoudong,Tao Wenbing,Wu Xianglin

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference39 articles.

1. Visual pattern discrimination;Julesz;IRE Transactions on Information Theory,1962

2. A parametric texture model based on joint statistics of complex wavelet coefficients;Portilla;International Journal of Computer Vision,2000

3. S. Awate, T. Tasdizen, R. Whitaker, Unsupervised texture segmentation with nonparametric neighborhood statistics, in: Proceedings of the European Conference on Computer Vision, 2006, pp. 494–507.

4. M. Varma, A. Zisserman, Texture classification: are filter banks necessary? in: Proceedings of Computer Vision and Pattern Recognition, 2003, pp. 691–698.

5. Unsupervised texture segmentation using Markov random field models;Manjunath;IEEE Transactions on Pattern Analysis and Machine Intelligence,1991

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