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Generalized ICM for image segmentation based on Tsallis statistics

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  • Kilic, Ilker
  • Kayacan, Ozhan

Abstract

In this paper, the iterated conditional modes optimization method of a Markov random field technique for image segmentation is generalized based on Tsallis statistics. It is observed that, for some q entropic index values the new algorithm performs better segmentation than the classical one. The proposed algorithm also does not have a local minimum problem and reaches a global minimum energy point although the number of iterations remains the same as ICM. Based on the findings of the new algorithm, it can be expressed that the new technique can be used for the image segmentation processes in which the objects are Gaussian or nearly Gaussian distributed.

Suggested Citation

  • Kilic, Ilker & Kayacan, Ozhan, 2012. "Generalized ICM for image segmentation based on Tsallis statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4899-4908.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:20:p:4899-4908
    DOI: 10.1016/j.physa.2011.12.062
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    References listed on IDEAS

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    1. Kilic, Ilker & Kayacan, Ozhan, 2007. "A new nonlinear quantizer for image processing within nonextensive statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 420-430.
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    Cited by:

    1. Ma, Tinghuai & Li, Lu & Ji, Sai & Wang, Xin & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Rodhaan, Mznah, 2014. "Optimized Laplacian image sharpening algorithm based on graphic processing unit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 400-410.
    2. Soares, H.C. & Meireles, J.B. & Castro, A.O. & Huguenin, J.A.O. & Schmidt, A.G.M. & da Silva, L., 2015. "Tsallis threshold analysis of digital speckle patterns generated by rough surfaces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 1-8.
    3. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.

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