Tsallis Entropy for Image Thresholding

Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

One interesting functional criterion for image thresholding is the Tsallis entropy, which gives excellent results in bi-level segmentation. However, when it is applied to multilevel thresholding, its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. Therefore, in the process of finding the appropriate threshold values, it is desired to limit the number of evaluations of the objective function. Under such circumstances, most of the optimization algorithms do not seem to be suited to face such problems as they usually require many evaluations before delivering an acceptable result. This chapter introduces the use of evolutionary algorithms to improve segmentation process using the Tsallis entropy for search the best thresholds.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages101-123
Number of pages23
DOIs
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume825
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Oliva, D., Abd Elaziz, M., & Hinojosa, S. (2019). Tsallis Entropy for Image Thresholding. In Studies in Computational Intelligence (pp. 101-123). (Studies in Computational Intelligence; Vol. 825). Springer Verlag. https://doi.org/10.1007/978-3-030-12931-6_9