30 October 2009 Texture image segmentation using Brushlet-domain hidden Markov models
Author Affiliations +
Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 749854 (2009) https://doi.org/10.1117/12.833002
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
By researching the Brushlet domain coefficients of texture images, we found that the distribution of the magnitudes of Brushlet domain coefficients roughly meet rayleigh distribution. And there are correlations between Brushlet coefficients in adjacent scales. Therefore, Rayleigh Mixture Model (RMM) is used to characterize the statistics of the magnitudes of Brushlet coefficients. To capture the inter-scale persistence of Brushlet coefficients, a "four to four" models with markov property is adopted in this paper. On the basis, by combining with the multi-scale Bayesian segmentation method, we propose a multiscale Bayesian texture segmentation algorithm that is based on a Brushlet domain hidden Markov tree (BruHMT) model. The experiment results indicate that our method is feasible and effective. Especially for coarse texture, our method is superior than texture segmentation method using Wavelet domain hidden Markov tree (WHMT) model.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fang Liu, Fang Liu, Kai Yang, Kai Yang, Hongxia Hao, Hongxia Hao, Biao Hou, Biao Hou, Hua Zhong, Hua Zhong, } "Texture image segmentation using Brushlet-domain hidden Markov models", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749854 (30 October 2009); doi: 10.1117/12.833002; https://doi.org/10.1117/12.833002
PROCEEDINGS
8 PAGES


SHARE
Back to Top