Fuzzy Entropy Approaches for Image Segmentation

Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)

Abstract

Images extracted from uncontrolled environments possesses different complexities that affects processing tasks. It also demerit the performance of segmentation approaches in specific when are used thresholding mechanism. The alternative is to use methods that are able to manage uncertainties and ambiguities presented in the pixel’s classification. Fuzzy entropy methods then are interesting alternatives that permits to handle the situations described above. This chapter then introduces the concepts that permits to use evolutionary algorithms to find the best configuration of fuzzy entropy approaches for images segmentation. This chapter is theoretical, but it also encourages the reader to experiment and apply the concepts using any evolutionary approach.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages141-147
Number of pages7
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). Fuzzy Entropy Approaches for Image Segmentation. In Studies in Computational Intelligence (pp. 141-147). (Studies in Computational Intelligence; Vol. 825). Springer Verlag. https://doi.org/10.1007/978-3-030-12931-6_11