基于隐马尔可夫模型的文本图像贝叶斯分离
Bayesian Separation of Document Images with Hidden Markov Model
摘要：
本文在贝叶斯框架下研究了含噪瞬时线性混合文档图像的分离问题。源图像是分层建模的潜在的标签过程中表示不同的颜色通道和像素的强度过程中给定的类标签之间的文档对象的共同分类。一个波茨马尔可夫随机场被用来模拟区域内的分类标签的区域规律性。相邻像素之间的局部依赖性也可以通过对它们的强度的平滑度约束来解释。在贝叶斯方法中，所有未知数，包括这些变量的来源，分类，混合系数和分布参数估计从他们的后验定律。相应的贝叶斯计算采用MCMC抽样算法。合成和实像混合物的实验结果来说明所提出的方法的性能。
英文摘要：
this paper we consider the problem of separating noisy instantaneous linear mixtures of document images in the Bayesian framework. The source image is modeled hierarchically by a latent labeling process representing the common classifications of document objects among different color channels and the intensity process of pixels given the class labels. A Potts Markov random field is used to model regional regularity of the classification labels inside object regions. Local dependency between neighboring pixels can also be accounted by smoothness constraint on their intensities. Within the Bayesian approach, all unknowns including the source, the classification, the mixing coefficients and the distribution parameters of these variables are estimated from their posterior laws. The corresponding Bayesian computations are done by MCMC sampling algorithm. Results from experiments on synthetic and real image mixtures are presented to illustrate the performance of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
