期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2015
卷号：6
期号：4
页码：71
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：A novel method is proposed for image segmentation based on probabilistic field theory. This modelassumes that the whole pixels of an image and some unknown parameters form a field. According to thismodel, the pixel labels are generated by a compound function of the field. The main novelty of this model isit consider the features of the pixels and the interdependent among the pixels. The parameters aregenerated by a novel spatially variant mixture model and estimated by expectation-maximization (EM)-based algorithm. Thus, we simultaneously impose the spatial smoothness on the prior knowledge.Numerical experiments are presented where the proposed method and other mixture model-based methodswere tested on synthetic and real world images. These experimental results demonstrate that our algorithmachieves competitive performance compared to other methods.
