基于证据马尔可夫随机场模型的图像分割
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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.
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.
引文
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[2]Tian L F,Slaughter D C.Environmentally adaptive segmentation algorithm for outdoor image segmentation[J].Computers and Electronics in Agriculture,1998,21(3):153-168.
[3]Zavaljevski A,Dhawan A P.Multi-level adaptive segmentation of multi-parameter MR brain images[J].Computerized Medical Imaging and Graphics,2002,24(2):87-98.
[4]Baik S W,Ahn S M.Adaptive segmentation of remote-sensing image for aerial surveillance[J].Lecture Notes in Computer Science,2003,2756:549-554.
[5]Sahoo P K,Soltani S,Wong A K C.A survey of thresholding techniques[J].Computer Vision,Graphics,and Image Processing,1988,41(2):233–260.
[6]Basak J,Chanda B,Manjumder D.On edge and line linking with connectionist models[J].IEEE Trans Systems,Man,and Cybernet,1994,24(3):413–428.
[7]Hojjatoleslami S A,Kittler J.Region growing:A new approach[J].IEEE Trans on Image Processing,1998,7(7):1079–1084.
[8]Haris K,Efstratiadis S N,Maglaveras N,et al.Hybrid image segmentation using watersheds and fast region merging[J].IEEE Trans on Image Processing,1998,7(12):1684–1699.
[9]Sarkar A,Biswas M K,Sharma M K S.A simple unsupervised MRF model based image segmentation approach[J].IEEE Trans on Image Processing,2000,9(6):801–812.
[10]Kato Z,Pong T C,Qiang S G.Multicue MRF image segmentation:combining texture and color features[C].The 16th Int Conf on Pattern Recognition.Turkey:IEEE Press,2002:10660.
[11]Chittajallu D R,Shah S K,Kakadiaris I A.A shape-driven MRF model for the segmentation of organs in medical images[C].IEEE Conf on Computer Vision and Pattern Recognition.San Francisco:IEEE Press,2010:3233–3240.
[12]Salzenstein F,Pieczynski W.Parameter estimation in hidden fuzzy Markov random fields and image segmentation[J].Graphical Models and Image Processing,1997,59(4):205-220.
[13]Shafer G,A Mathematical theory of evidence[J].Technometrics,1978,20(1):242.
[14]Dempster A P.Upper and lower probabilities induced by a multiple valued mapping[J].The Annals of Mathematical Statistics,1967,38(2):325–339.
[15]Bendjebbour A,Delignon Y.Multisensor image segmentation using Dempster-Shafer fusion in Markov feilds context[J].IEEE Trans on Geoscience and Remote Sensing,2001,39(8):1789-1798.
[16]Rafael C.Gonzalez,Richard E Woods.Digital image Processing[M].Beijing:Publishing House of Electronics Industry,2010:712-713.
[17]Besag J.Spatial interaction the statistical analysis of lattice systems[J].J of the Royal Statistical Society,1974,36(2):192–236.
[18]Geman S,Geman D.Stochastic relaxation,Gibbs distribution,and the Bayesian restoration of images[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1984,6(6):721–741.
[19]Jousselme A L,Grenier D.A new distance between two bodies of evidence[J].Information Fusion,2001,2(2):91–101.
[20]Masson M H,Denoeux T.ECM:An evidential version of the fuzzy c-means algorithm[J].Pattern Recognition,2008,41(4):1384-1397.
[21]Zhang Zebing,Hu Weidong.Hyper parameter estimation in MRF based SAR chip image segmentation[C].IEEE CIE Int Conf on Radar.Chengdu:IEEE Press,2011:760–763.