Author Description

<p><b>Introduces quantum inspired techniques for image analysis for pure and true gray scale/color images in a single/multi-objective environment</b></p> <p>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.</p> <p><i>Quantum Inspired Meta-heuristics for Image Analysis</i> 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.</p> <ul> <li>Provides in-depth analysis of quantum mechanical principles</li> <li>Offers comprehensive review of image analysis</li> <li>Analyzes different state-of-the-art image thresholding approaches</li> <li>Detailed current, popular standard meta-heuristics in use today</li> <li>Guides readers step by step in the build-up of quantum inspired meta-heuristics</li> <li>Includes a plethora of real life case studies and applications</li> <li>Features statistical test analysis of the performances of the quantum inspired meta-heuristics vis-à-vis their conventional counter