Название: Quantum Inspired Meta-heuristics for Image Analysis
Автор: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik
Издательство: Wiley
Год: 2019
Страниц: 367
Язык: английский
Формат: pdf (true)
Размер: 37.6 MB

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.

In the present information era, the processing and retrieval of useful image information and multimedia-based data, for the purpose of faithful and realistic analysis, are supposed to be of the highest importance. One significant image processing chore is to separate objects or other important information in digital images through thresholding of the image under consideration. Efficient techniques are required in order to develop an appropriate analysis of noisy and noise-free image data to obtain suitable object-specific information.

The soft computing approaches have certain tools and techniques among various other approaches, which integrate intelligent thinking and principles. Fuzzy logic, Neural networks, Fuzzy sets, and Evolutionary Computation are used as the computing framework, which successfully combines these intelligent principles.

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-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.