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Advances and Applications of Optimised Algorithms in Image Processing

  • Book
  • © 2017

Overview

Authors:
  1. Diego Oliva

    1. Tecnológico de Monterrey, Campus Guadalajara, , Guadajalara, Mexico

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    You can also search for this author in PubMed   Google Scholar

  2. Erik Cuevas

    1. University of Guadajalara , Guadajalara, Mexico

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Provides explanations of the algorithms used, selected problems and implementations, as well as practical examples, comparisons and experimental results
  • All chapters are self-contained and can all be read separately
  • Offers a compendium of the most interesting electromagnetism applications, e.g. optimization (EMO)-based algorithms for image processing problems
  • Guides the reader from the basics of optimization to different case studies
  • Includes supplementary material: sn.pub/extras

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 117)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. An Introduction to Machine Learning

    • Diego Oliva, Erik Cuevas
    Pages 1-11

  3. Optimization

    • Diego Oliva, Erik Cuevas
    Pages 13-21

  4. Electromagnetism—Like Optimization Algorithm: An Introduction

    • Diego Oliva, Erik Cuevas
    Pages 23-41

  5. Digital Image Segmentation as an Optimization Problem

    • Diego Oliva, Erik Cuevas
    Pages 43-91

  6. Template Matching Using a Physical Inspired Algorithm

    • Diego Oliva, Erik Cuevas
    Pages 93-111

  7. Detection of Circular Shapes in Digital Images

    • Diego Oliva, Erik Cuevas
    Pages 113-134

  8. A Medical Application: Blood Cell Segmentation by Circle Detection

    • Diego Oliva, Erik Cuevas
    Pages 135-157

  9. An EMO Improvement: Opposition-Based Electromagnetism-Like for Global Optimization

    • Diego Oliva, Erik Cuevas
    Pages 159-178
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Keywords

About this book

This book presents a study of the use of optimization algorithms in complex image processing problems. The problems selected explore areas ranging from the theory of image segmentation to the detection of complex objects in medical images. Furthermore, the concepts of machine learning and optimization are analyzed to provide an overview of the application of these tools in image processing.

 

The material has been compiled from a teaching perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, and can be used for courses on Artificial Intelligence, Advanced Image Processing, Computational Intelligence, etc. Likewise, the material can be useful for research from the evolutionary computation, artificial intelligence and image processing communities.

Reviews

“This book examines the application of optimization to common problems in image processing: segmentation, template matching, and shape detection. … Readers interested in improving the accuracy or performance of image processing systems would do well to read this book.” (Creed Jones, Computing Reviews, May, 2017)

Authors and Affiliations

  • Tecnológico de Monterrey, Campus Guadalajara, , Guadajalara, Mexico

    Diego Oliva

  • University of Guadajalara , Guadajalara, Mexico

    Erik Cuevas

Bibliographic Information

  • Book Title: Advances and Applications of Optimised Algorithms in Image Processing

  • Authors: Diego Oliva, Erik Cuevas

  • Series Title: Intelligent Systems Reference Library

  • DOI: https://doi.org/10.1007/978-3-319-48550-8

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG 2017

  • Hardcover ISBN: 978-3-319-48549-2Published: 30 November 2016

  • Softcover ISBN: 978-3-319-83969-1Published: 05 July 2018

  • eBook ISBN: 978-3-319-48550-8Published: 21 November 2016

  • Series ISSN: 1868-4394

  • Series E-ISSN: 1868-4408

  • Edition Number: 1

  • Number of Pages: XIII, 178

  • Number of Illustrations: 53 b/w illustrations

  • Topics: Computational Intelligence, Signal, Image and Speech Processing, Artificial Intelligence

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