Bayesian Blind Separation of Mized Tezt Patterns
In this paper we consider the problem of unsupervised separation of mixed text patterns based on blind source separation models. We propose a hierarchical Markov random field model for the source patterns, which enforces piece-wise regularity on both labels and intensities of image pixels. We also presented a hierarchical Bayesian BSS framework, in which the unknown sources and labels is estimated through a generic iterative algorithm framework on the basis of corresponding posterior laws. Experiment results on synthetic and real sample images are presented to show the feasibility of the proposed model.
Feng Su Shijie Cai Ali MOHAMMAD-DJAFARI
State Key Laboratory for Novel, Software Technology, Nanjing University Nanjing, 210093, P.R.China Laboratoire des Signaux et Systemes, UMR 8506 (CNRS-Supelec-UPS), Supelec Gif-sur-Yvette, 91192, Fra
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
1373-1378
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）