Image segmentation based on evidential Markov random field model

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 uncertainty encountered in the image segmentation, an evidential Markov random field (EMRF) model is designed, based on which a novel image segmentation algorithm is proposed in this paper. The credal partition based on the evidence theory is used to define the label field. The iterated conditional modes (ICM) algorithm is used for the optimization in EMRF. Experimental results show that our proposed algorithm can provide a better segmentation result against the traditional MRF, the Fuzzy MRF (FMRF) and the traditonal evidential approaches.

[1]  Sung Wook Baik,et al.  Adaptive Segmentation of Remote-Sensing Images for Aerial Surveillance , 2003, CAIP.

[2]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[3]  Wojciech Pieczynski,et al.  Parameter Estimation in Hidden Fuzzy Markov Random Fields and Image Segmentation , 1997, CVGIP Graph. Model. Image Process..

[4]  Zoltan Kato,et al.  Multicue MRF image segmentation: combining texture and color features , 2002, Object recognition supported by user interaction for service robots.

[5]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[6]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[7]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[8]  Josef Kittler,et al.  Region growing: a new approach , 1998, IEEE Trans. Image Process..

[9]  Ioannis A. Kakadiaris,et al.  A shape-driven MRF model for the segmentation of organs in medical images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Bhabatosh Chanda,et al.  On edge and line linking with connectionist models , 1994, IEEE Trans. Syst. Man Cybern..

[11]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[12]  A Zavaljevski,et al.  Multi-level adaptive segmentation of multi-parameter MR brain images. , 2000, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[13]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Thierry Denoeux,et al.  ECM: An evidential version of the fuzzy c , 2008, Pattern Recognit..

[15]  Aggelos K. Katsaggelos,et al.  Hybrid image segmentation using watersheds and fast region merging , 1998, IEEE Trans. Image Process..

[16]  Lei Guo,et al.  A Novel MRF-Based Image Segmentation Algorithm , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[17]  Thierry Denoeux,et al.  Clustering interval-valued proximity data using belief functions , 2004, Pattern Recognit. Lett..

[18]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[19]  Chia-Feng Juang,et al.  Computer Vision-Based Human Body Segmentation and Posture Estimation , 2009, SMC 2009.

[20]  Anjan Sarkar,et al.  A simple unsupervised MRF model based image segmentation approach , 2000, IEEE Trans. Image Process..

[21]  Thierry Denoeux,et al.  EVCLUS: evidential clustering of proximity data , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Lei Tian,et al.  Environmentally adaptive segmentation algorithm for outdoor image segmentation , 1998 .

[23]  Wojciech Pieczynski,et al.  Multisensor image segmentation using Dempster-Shafer fusion in Markov fields context , 2001, IEEE Trans. Geosci. Remote. Sens..

[24]  Éloi Bossé,et al.  A new distance between two bodies of evidence , 2001, Inf. Fusion.

[25]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[26]  T. Denœux,et al.  Clustering of proximity data using belief functions , 2003 .