基于证据马尔可夫随机场模型的图像分割  

Image segmentation based on evidential Markov random field model

作　　者：张喆[1,2] 韩德强[1,2] 杨艺[3,4] 

机构地区：[1]西安交通大学智能网络与网络安全教育部重点实验室,西安710049 [2]西安交通大学电子与信息工程学院,西安710049 [3]西安交通大学机械振动与强度国家重点实验室,西安710049 [4]西安交通大学航天航空学院,西安710049

出　　处：《控制与决策》2017年第9期1607-1613,共7页Control and Decision

基　　金：国家973计划项目(2013CB329405);国家自然科学基金项目(61573275;61671370);陕西省科技计划项目(2013KJXX-46);中央高校基本科研业务费专项资金项目(xjj2016066;xjj2014122)

摘　　要：图像分割是计算机视觉中的经典问题,在许多领域都有重要应用.由于图像信息存在不确定性,难以获得精确的分割结果,为应对图像分割中的不确定性问题,将证据理论这一不确定性建模与推理工具与马尔可夫随机场相结合,提出证据马尔可夫随机场(EMRF)模型,并基于此提出新的图像分割算法.EMRF利用证据标号场描述像素标号的含混性,以证据距离描述相邻像素间的标号关系,利用条件迭代模型(ICM)算法进行优化.实验结果表明,EMRF相较于传统马尔可夫随机场、模糊马尔可夫随机场和传统的基于证据理论的方法,能获得更好的分割效果.Image segmentation is a classical problem in computer vision and has been widely used in many fields. Due to the uncertainty in images, it is difficult to obtain a precise segmentation result. To deal with the problem of the uncertainty encountered in the image segmentation, an evidential Markov random field（EMRF） model is designed, which combines the evidence theory, a powerful tool for modeling and reasoning uncertainty, with the Markov random field. Based on EMRF, a novel image segmentation algorithm is proposed. EMRF uses evidential label field to describe the ambiguity of labels and distance of evidence to describe the relationship between labels of the neighboring pixels. The iterated conditional modes（ICM） algorithm is used for optimization. Experimental results show that the proposed algorithm can provide a better segmentation result against traditional MRF, fuzzy MRF（FMRF） and traditional evidential approaches.

关 键 词：图像分割 证据理论 证据马尔可夫随机场 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]