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Applications of Hybrid Metaheuristic Algorithms for Image Processing

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  • © 2020

Overview

Editors:
  1. Diego Oliva

    1. CUCEI, University of Guadalajara, Guadajalara, Mexico

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  2. Salvador Hinojosa

    1. CUCEI, University of Guadalajara, Guadajalara, Mexico

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    You can also search for this editor in PubMed   Google Scholar

  • Presents the latest tools used for image processing and hybrid metaheuristic algorithms
  • Focuses on the theory and application of metaheuristic algorithms for segmentation of images from different sources
  • Discusses recent research on and applications of hybrid metaheuristic algorithms for image processing

Part of the book series: Studies in Computational Intelligence (SCI, volume 890)

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Table of contents (22 chapters)

  1. Front Matter

    Pages i-ix
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  2. Hybrid Metaheuristics and Image Segmentation

    1. Front Matter

      Pages 1-1
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    2. Segmentation of Thermal Images Using Metaheuristic Algorithms for Failure Detection on Electronic Systems

      • Mario A. Navarro, Gustavo R. Hernández, Daniel Zaldívar, Noé Ortega-Sanchez, Gonzalo Pajares
      Pages 3-26

    3. A Survey on Image Processing for Hyperspectral and Remote Sensing Images

      • Alfonso Ramos-Michel, Marco Pérez-Cisneros, Erik Cuevas, Daniel Zaldivar
      Pages 27-51

    4. Hybrid Grey-Wolf Optimizer Based Fractional Order Optimal Filtering for Texture Aware Quality Enhancement for Remotely Sensed Images

      • Himanshu Singh, Anil Kumar, L. K. Balyan
      Pages 53-79

    5. Robust K-Means Technique for Band Reduction of Hyperspectral Image Segmentation

      • V. Saravana Kumar, E. R. Naganathan, S. Anantha Sivaprakasam, M. Kavitha
      Pages 81-103

    6. RETRACTED CHAPTER: Ethnic Characterization in Amalgamated People for Airport Security Using a Repository of Images and Pigeon-Inspired Optimization (PIO) Algorithm for the Improvement of Their Results

      • Alberto Ochoa-Zezzatti, José Mejía, Roberto Contreras-Masse, Erwin Martínez, Andrés Hernández
      Pages 105-119

    7. Multi-level Image Thresholding Segmentation Using 2D Histogram Non-local Means and Metaheuristics Algorithms

      • Andrea A. Hernandez del Rio, Erik Cuevas, Daniel Zaldivar
      Pages 121-149

  3. Hybrid Metaheuristics and Other Image Processing Tasks

    1. Front Matter

      Pages 151-151
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    2. Comparison of Metaheuristic Methods for Template Matching

      • Gemma Corona, Marco Pérez-Cisneros, Oscar Maciel-Castillo, Adrián González, Fernando Fausto
      Pages 153-166

    3. Novel Feature Extraction Strategies Supporting 2D Shape Description and Retrieval

      • P. Govindaraj, M. S. Sudhakar
      Pages 167-185

    4. Clustering Data Using Techniques of Image Processing Erode and Dilate to Avoid the Use of Euclidean Distance

      • Noé Ortega-Sánchez, Erik Cuevas, Marco A. Pérez, Valentín Osuna-Enciso
      Pages 187-203

    5. Estimation of the Homography Matrix to Image Stitching

      • Cesar Ascencio
      Pages 205-230

    6. Active Contour Model in Deep Learning Era: A Revise and Review

      • T. Hoang Ngan Le, Khoa Luu, Chi Nhan Duong, Kha Gia Quach, Thanh Dat Truong, Kyle Sadler et al.
      Pages 231-260

    7. Linear Regression Techniques for Car Accident Prediction

      • Miguel Islas Toski, Karla Avila-Cardenas, Jorge Gálvez
      Pages 261-284

    8. Salp Swarm Algorithm: A Comprehensive Review

      • Essam H. Houssein, Ibrahim E. Mohamed, Yaser M. Wazery
      Pages 285-308

  4. Health Applications

    1. Front Matter

      Pages 309-309
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    2. Segmentation of Magnetic Resonance Brain Images Through the Self-Adaptive Differential Evolution Algorithm and the Minimum Cross-Entropy Criterion

      • Itzel Aranguren, Arturo Valdivia, Marco A. Pérez
      Pages 311-350

    3. Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

      • Erick Rodrí­guez-Esparza, Laura A. Zanella-Calzada, Daniel Zaldivar, Carlos E. Galván-Tejada
      Pages 351-374

    4. Cancer Cell Prediction Using Machine Learning and Evolutionary Algorithms

      • Karla Avila-Cardenas, Marco Pérez-Cisneros
      Pages 375-398
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Keywords

About this book

This book presents a collection of the most recent hybrid methods for image processing. The algorithms included consider evolutionary, swarm, machine learning and deep learning. The respective chapters explore different areas of image processing, from image segmentation to the recognition of objects using complex approaches and medical applications. The book also discusses the theory of the methodologies used to provide an overview of the applications of these tools in image processing.

The book is primarily intended for undergraduate and postgraduate students of science, engineering and computational mathematics, and can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence. Further, it is a valuable resource for researchers from the evolutionary computation, artificial intelligence and image processing communities.


Editors and Affiliations

  • CUCEI, University of Guadalajara, Guadajalara, Mexico

    Diego Oliva, Salvador Hinojosa

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