Quantum Inspired Meta-Heuristics for Image Analysis

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Quantum Inspired Meta-Heuristics for Image Analysis

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內容描述

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 an in-depth analysis of quantum mechanical principles
Offers a comprehensive review of image analysis
Analyzes different state-of-the-art image thresholding approaches
Details 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-à-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.

作者介紹

SANDIP DEY, PHD, is an Associate Professor and Chair in the department of Computer Science & Engineering at the Global Institute of Management and Technology, Krishnanagar, Nadia, West Bengal, India.
SIDDHARTHA BHATTACHARYYA, PHD, is the Principal of RCC Institute of Information Technology, Kolkata, India.
UJJWAL MAULIK, PHD, is the Chair of and Professor in the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.