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Metaheuristic Algorithms for Image Segmentation: Theory and Applications

  • Book
  • © 2019

Overview

Authors:
  1. Diego Oliva

    1. Departamento de Electrónica, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

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  2. Mohamed Abd Elaziz

    1. Faculty of Science, Zagazig University, Zagazig, Egypt

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    You can also search for this author in PubMed   Google Scholar

  3. Salvador Hinojosa

    1. Facultad de Informática, Universidad Complutense de Madrid, Madrid, Spain

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  • Provides the most representative tools used for image segmentation
  • Examines the theory and application of metaheuristics algorithms for the segmentation of images from diverse sources
  • Presents a compendium of methods useful for students, scientists and practitioners
  • Includes self-contained chapters that explain the algorithm used, the selected problem, and the implementation
  • Offers practical examples, comparisons, and experimental results
  • Focuses on lightweight segmentation methods based on thresholding techniques using metaheuristics algorithms (MA) to perform the pre-processing step for CVS

Part of the book series: Studies in Computational Intelligence (SCI, volume 825)

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Introduction

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 1-5

  3. Optimization

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 7-11

  4. Metaheuristic Optimization

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 13-26

  5. Image Processing

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 27-45

  6. Image Segmentation Using Metaheuristics

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 47-58

  7. Multilevel Thresholding for Image Segmentation Based on Metaheuristic Algorithms

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 59-69

  8. Otsu’s Between Class Variance and the Tree Seed Algorithm

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 71-83

  9. Image Segmentation Using Kapur’s Entropy and a Hybrid Optimization Algorithm

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 85-99

  10. Tsallis Entropy for Image Thresholding

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 101-123

  11. Image Segmentation with Minimum Cross Entropy

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 125-139

  12. Fuzzy Entropy Approaches for Image Segmentation

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 141-147

  13. Image Segmentation by Gaussian Mixture

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 149-155

  14. Image Segmentation as a Multiobjective Optimization Problem

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 157-179

  15. Clustering Algorithms for Image Segmentation

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 181-189

  16. Contextual Information in Image Thresholding

    • Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa
    Pages 191-226
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Keywords

About this book

This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designedto solve complex optimization problems increases. 
This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.

Authors and Affiliations

  • Departamento de Electrónica, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Diego Oliva

  • Faculty of Science, Zagazig University, Zagazig, Egypt

    Mohamed Abd Elaziz

  • Facultad de Informática, Universidad Complutense de Madrid, Madrid, Spain

    Salvador Hinojosa

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