Color image segmentation based on Dempster-Shafer evidence theory

In this paper, a color image segmentation approach based on Dempster-Shafer evidence theory is presented. The basic technique consists in combining information coming from three independent information sources for the same image. These sources correspond to the three component images R (red), G (green) and B (blue). The Dempster-Shafer theory of evidence is applied in order to fuse the information from these three sources. This method shows the spectacular ability of the evidence theory to handling uncertain, imprecise and incomplete information. The Results on cell images are presented in order to demonstrate the effectiveness of the proposed method.

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

[2]  H. Zimmermann,et al.  Quantifying vagueness in decision models , 1985 .

[3]  Derek R. Peddle,et al.  An Empirical Comparison of Evidential Reasoning, Linear Discriminant Analysis, and Maximum Likelihood Algorithms for Alpine Land Cover Classification , 1993 .

[4]  Philippe Smets,et al.  Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem , 1993, Int. J. Approx. Reason..

[5]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[6]  Sang Uk Lee,et al.  Color image segmentation based on 3-D clustering: morphological approach , 1998, Pattern Recognit..

[7]  Isabelle Bloch,et al.  Fusion of Image Information under Imprecision , 1998 .

[8]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[9]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[10]  Pau-Choo Chung,et al.  Color image segmentation using fuzzy C-means and eigenspace projections , 2002, Signal Process..

[11]  Jayanta Mukherjee.  MRF clustering for segmentation of color images , 2002, Pattern Recognit. Lett..

[12]  J. Tasic,et al.  Colour spaces: perceptual, historical and applicational background , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[13]  Neng-Hai Yu,et al.  Multiple level parallel decision fusion model with distributed sensors based on Dempster-Shafer evidence theory , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[14]  Kenji Suzuki,et al.  Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector , 2004, IEEE Transactions on Medical Imaging.

[15]  A. Martin.  Comparative study of information fusion methods for sonar images classification , 2005, 2005 7th International Conference on Information Fusion.

[16]  Amir Averbuch,et al.  Color image segmentation based on adaptive local thresholds , 2005, Image Vis. Comput..

[17]  Didier Dubois,et al.  Possibility Theory and its Applications: a Retrospective and Prospective view , 2006, Decision Theory and Multi-Agent Planning.

[18]  Andreas Koschan,et al.  Color Image Segmentation , 2007 .