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Tsallis Entropy In Bi-level And Multi-level Image Thresholding

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  • Amelia Carolina Sparavigna

Abstract

The maximum entropy principle has a relevant role in image processing, in particular for thresholding and image segmentation. Different entropic formulations are available to this purpose; one of them is based on the Tsallis non-extensive entropy. Here, we propose a discussion of its use for bi- and multi-level thresholding.

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  • Amelia Carolina Sparavigna, 2015. "Tsallis Entropy In Bi-level And Multi-level Image Thresholding," International Journal of Sciences, Office ijSciences, vol. 4(01), pages 40-49, January.
    • Handle: RePEc:adm:journl:v:4:y:2015:i:1:p:40-49
    DOI: 10.18483/ijSci.613
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    References listed on IDEAS

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    1. Fabbri, Ricardo & Gonçalves, Wesley N. & Lopes, Francisco J.P. & Bruno, Odemir M., 2012. "Multi-q pattern analysis: A case study in image classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4487-4496.
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    Cited by:

    1. Amelia Carolina Sparavigna, 2015. "Gray-Level Image Transitions Driven by Tsallis Entropic Index," International Journal of Sciences, Office ijSciences, vol. 4(02), pages 16-25, February.
    2. Amelia Carolina Sparavigna, 2017. "Image Segmentation Applied to the Study of Micrographs of Cellular Solids," International Journal of Sciences, Office ijSciences, vol. 6(02), pages 68-76, February.

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