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Quantum inspired meta-heuristics for image analysis [electronic resource] / Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik.

By: Contributor(s): Material type: TextTextPublisher: Hoboken, NJ : John Wiley & Sons, Inc., 2019Copyright date: ©2019Description: 1 online resource ( xvi, 358 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119488781
  • 1119488788
  • 9781119488774
  • 111948877X
  • 9781119488767
  • 1119488761
Subject(s): Genre/Form: Additional physical formats: Print version:: Quantum inspired meta-heuristics for image analysisDDC classification:
  • 006.4/2015181 23
LOC classification:
  • TA1638.4 .D49 2019
Online resources: Summary: Introduces quantum inspired techniques for image analysis for pure and true gray scale/color images in a single/multi-objective environment This book will entice readers to design efficient meta-heuristics for image analysis in the quantum domain. It introduces them to the essence of quantum computing paradigm, its features, and properties, and elaborates on the fundamentals of different meta-heuristics and their application to image analysis. As a result, it will pave the way for designing and developing quantum computing inspired meta-heuristics to be applied to image analysis. Quantum Inspired Meta-heuristics for Image Analysis begins with a brief summary on image segmentation, quantum computing, and optimization. It also highlights a few relevant applications of the quantum based computing algorithms, meta-heuristics approach, and several thresholding algorithms in vogue. Next, it discusses a review of image analysis before moving on to an overview of six popular meta-heuristics and their algorithms and pseudo-codes. Subsequent chapters look at quantum inspired meta-heuristics for bi-level and gray scale multi-level image thresholding; quantum behaved meta-heuristics for true color multi-level image thresholding; and quantum inspired multi-objective algorithms for gray scale multi-level image thresholding. Each chapter concludes with a summary and sample questions. -Provides in-depth analysis of quantum mechanical principles -Offers comprehensive review of image analysis -Analyzes different state-of-the-art image thresholding approaches -Detailed current, popular standard meta-heuristics in use today -Guides readers step by step in the build-up of quantum inspired meta-heuristics -Includes a plethora of real life case studies and applications -Features statistical test analysis of the performances of the quantum inspired meta-heuristics vis-A-vis their conventional counterparts Quantum Inspired Meta-heuristics for Image Analysis is an excellent source of information for anyone working with or learning quantum inspired meta-heuristics for image analysis.
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Includes bibliographical references and index.

Description based on online resource; title from digital title page (viewed on August 14, 2019).

Introduces quantum inspired techniques for image analysis for pure and true gray scale/color images in a single/multi-objective environment This book will entice readers to design efficient meta-heuristics for image analysis in the quantum domain. It introduces them to the essence of quantum computing paradigm, its features, and properties, and elaborates on the fundamentals of different meta-heuristics and their application to image analysis. As a result, it will pave the way for designing and developing quantum computing inspired meta-heuristics to be applied to image analysis. Quantum Inspired Meta-heuristics for Image Analysis begins with a brief summary on image segmentation, quantum computing, and optimization. It also highlights a few relevant applications of the quantum based computing algorithms, meta-heuristics approach, and several thresholding algorithms in vogue. Next, it discusses a review of image analysis before moving on to an overview of six popular meta-heuristics and their algorithms and pseudo-codes. Subsequent chapters look at quantum inspired meta-heuristics for bi-level and gray scale multi-level image thresholding; quantum behaved meta-heuristics for true color multi-level image thresholding; and quantum inspired multi-objective algorithms for gray scale multi-level image thresholding. Each chapter concludes with a summary and sample questions. -Provides in-depth analysis of quantum mechanical principles -Offers comprehensive review of image analysis -Analyzes different state-of-the-art image thresholding approaches -Detailed current, popular standard meta-heuristics in use today -Guides readers step by step in the build-up of quantum inspired meta-heuristics -Includes a plethora of real life case studies and applications -Features statistical test analysis of the performances of the quantum inspired meta-heuristics vis-A-vis their conventional counterparts Quantum Inspired Meta-heuristics for Image Analysis is an excellent source of information for anyone working with or learning quantum inspired meta-heuristics for image analysis.

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