Contextual Information in Image Thresholding

Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this chapter, an alternative to the traditional thresholding of images is presented for segmentation. Most threshold-based segmentation procedures use the histogram of the image as the only source of information to partition the image; although this approach perform well on most scenarios, it only relies on the intensity of the pixels while ignoring the spatial relationships. Contextual information can help to enhance the quality of the segmented images as it considers not only the value of the pixel but also its vicinity. The energy curve was designed to bring spatial information into a curve with the same properties as the histogram. Thus, most thresholding approaches can be directly applied to the energy curve. In this chapter, the performance of the segmentation of images using the energy curve is analyzed using the Ant-Lion Optimizer with both Otsu and Kapur methods.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages191-226
Number of pages36
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). Contextual Information in Image Thresholding. In Studies in Computational Intelligence (pp. 191-226). (Studies in Computational Intelligence; Vol. 825). Springer Verlag. https://doi.org/10.1007/978-3-030-12931-6_15