Quantum Inspired Meta-heuristics for Image Analysis

Name: Quantum Inspired Meta-heuristics for Image Analysis
Brand: Wiley-Blackwell
Price: 121.99 EUR
Availability: OnlineOnly

Sandip Dey Siddhartha Bhattacharyya Ujjwal Maulik(Autor*in)

Wiley-Blackwell (Verlag)

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Erschienen am 29. Mai 2019

6 Seiten

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Beschreibung

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-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.

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Inhalt

Cover
Title Page
Copyright
Contents
Preface
Acronyms
Chapter 1 Introduction
1.1 Image Analysis
1.1.1 Image Segmentation
1.1.2 Image Thresholding
1.2 Prerequisites of Quantum Computing
1.2.1 Dirac's Notation
1.2.2 Qubit
1.2.3 Quantum Superposition
1.2.4 Quantum Gates
1.2.4.1 Quantum NOT Gate (Matrix Representation)
1.2.4.2 Quantum Z Gate (Matrix Representation)
1.2.4.3 Hadamard Gate
1.2.4.4 Phase Shift Gate
1.2.4.5 Controlled NOT Gate (CNOT)
1.2.4.6 SWAP Gate
1.2.4.7 Toffoli Gate
1.2.4.8 Fredkin Gate
1.2.4.9 Quantum Rotation Gate
1.2.5 Quantum Register
1.2.6 Quantum Entanglement
1.2.7 Quantum Solutions of NP-complete Problems
1.3 Role of Optimization
1.3.1 Single-objective Optimization
1.3.2 Multi-objective Optimization
1.3.3 Application of Optimization to Image Analysis
1.4 Related Literature Survey
1.4.1 Quantum-based Approaches
1.4.2 Meta-heuristic-based Approaches
1.4.3 Multi-objective-based Approaches
1.5 Organization of the Book
1.5.1 Quantum Inspired Meta-heuristics for Bi-level Image Thresholding
1.5.2 Quantum Inspired Meta-heuristics for Gray-scale Multi-level Image Thresholding
1.5.3 Quantum Behaved Meta-heuristics for True Color Multi-level Thresholding
1.5.4 Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding
1.6 Conclusion
1.7 Summary
Exercise Questions
Chapter 2 Review of Image Analysis
2.1 Introduction
2.2 Definition
2.3 Mathematical Formalism
2.4 Current Technologies
2.4.1 Digital Image Analysis Methodologies
2.4.1.1 Image Segmentation
2.4.1.2 Feature Extraction/Selection
2.4.1.3 Classification
2.5 Overview of Different Thresholding Techniques
2.5.1 Ramesh's Algorithm
2.5.2 Shanbag's Algorithm
2.5.3 Correlation Coefficient
2.5.4 Pun's Algorithm
2.5.5 Wu's Algorithm
2.5.6 Renyi's Algorithm
2.5.7 Yen's Algorithm
2.5.8 Johannsen's Algorithm
2.5.9 Silva's Algorithm
2.5.10 Fuzzy Algorithm
2.5.11 Brink's Algorithm
2.5.12 Otsu's Algorithm
2.5.13 Kittler's Algorithm
2.5.14 Li's Algorithm
2.5.15 Kapur's Algorithm
2.5.16 Huang's Algorithm
2.6 Applications of Image Analysis
2.7 Conclusion
2.8 Summary
Exercise Questions
Chapter 3 Overview of Meta-heuristics
3.1 Introduction
3.1.1 Impact on Controlling Parameters
3.2 Genetic Algorithms
3.2.1 Fundamental Principles and Features
3.2.2 Pseudo-code of Genetic Algorithms
3.2.3 Encoding Strategy and the Creation of Population
3.2.4 Evaluation Techniques
3.2.5 Genetic Operators
3.2.6 Selection Mechanism
3.2.7 Crossover
3.2.8 Mutation
3.3 Particle Swarm Optimization
3.3.1 Pseudo-code of Particle Swarm Optimization
3.3.2 PSO: Velocity and Position Update
3.4 Ant Colony Optimization
3.4.1 Stigmergy in Ants: Biological Inspiration
3.4.2 Pseudo-code of Ant Colony Optimization
3.4.3 Pheromone Trails
3.4.4 Updating Pheromone Trails
3.5 Differential Evolution
3.5.1 Pseudo-code of Differential Evolution
3.5.2 Basic Principles of DE
3.5.3 Mutation
3.5.4 Crossover
3.5.5 Selection
3.6 Simulated Annealing
3.6.1 Pseudo-code of Simulated Annealing
3.6.2 Basics of Simulated Annealing
3.7 Tabu Search
3.7.1 Pseudo-code of Tabu Search
3.7.2 Memory Management in Tabu Search
3.7.3 Parameters Used in Tabu Search
3.8 Conclusion
3.9 Summary
Exercise Questions
Chapter 4 Quantum Inspired Meta-heuristics for Bi-level Image Thresholding
4.1 Introduction
4.2 Quantum Inspired Genetic Algorithm
4.2.1 Initialize the Population of Qubit Encoded Chromosomes
4.2.2 Perform Quantum Interference
4.2.2.1 Generate Random Chaotic Map for Each Qubit State
4.2.2.2 Initiate Probabilistic Switching Between Chaotic Maps
4.2.3 Find the Threshold Value in Population and Evaluate Fitness
4.2.4 Apply Selection Mechanism to Generate a New Population
4.2.5 Foundation of Quantum Crossover
4.2.6 Foundation of Quantum Mutation
4.2.7 Foundation of Quantum Shift
4.2.8 Complexity Analysis
4.3 Quantum Inspired Particle Swarm Optimization
4.3.1 Complexity Analysis
4.4 Implementation Results
4.4.1 Experimental Results (Phase I)
4.4.1.1 Implementation Results for QEA
4.4.2 Experimental Results (Phase II)
4.4.2.1 Experimental Results of Proposed QIGA and Conventional GA
4.4.2.2 Results Obtained with QEA
4.4.3 Experimental Results (Phase III)
4.4.3.1 Results Obtained with Proposed QIGA and Conventional GA
4.4.3.2 Results obtained from QEA
4.5 Comparative Analysis among the Participating Algorithms
4.6 Conclusion
4.7 Summary
Exercise Questions
Coding Examples
Chapter 5 Quantum Inspired Meta-Heuristics for Gray-Scale Multi-Level Image Thresholding
5.1 Introduction
5.2 Quantum Inspired Genetic Algorithm
5.2.1 Population Generation
5.2.2 Quantum Orthogonality
5.2.3 Determination of Threshold Values in Population and Measurement of Fitness
5.2.4 Selection
5.2.5 Quantum Crossover
5.2.6 Quantum Mutation
5.2.7 Complexity Analysis
5.3 Quantum Inspired Particle Swarm Optimization
5.3.1 Complexity Analysis
5.4 Quantum Inspired Differential Evolution
5.4.1 Complexity Analysis
5.5 Quantum Inspired Ant Colony Optimization
5.5.1 Complexity Analysis
5.6 Quantum Inspired Simulated Annealing
5.6.1 Complexity Analysis
5.7 Quantum Inspired Tabu Search
5.7.1 Complexity Analysis
5.8 Implementation Results
5.8.1 Consensus Results of the Quantum Algorithms
5.9 Comparison of QIPSO with Other Existing Algorithms
5.10 Conclusion
5.11 Summary
Exercise Questions
Coding Examples
Chapter 6 Quantum Behaved Meta-Heuristics for True Color Multi-Level Image Thresholding
6.1 Introduction
6.2 Background
6.3 Quantum Inspired Ant Colony Optimization
6.3.1 Complexity Analysis
6.4 Quantum Inspired Differential Evolution
6.4.1 Complexity Analysis
6.5 Quantum Inspired Particle Swarm Optimization
6.5.1 Complexity Analysis
6.6 Quantum Inspired Genetic Algorithm
6.6.1 Complexity Analysis
6.7 Quantum Inspired Simulated Annealing
6.7.1 Complexity Analysis
6.8 Quantum Inspired Tabu Search
6.8.1 Complexity Analysis
6.9 Implementation Results
6.9.1 Experimental Results (Phase I)
6.9.1.1 The Stability of the Comparable Algorithms
6.9.2 The Performance Evaluation of the Comparable Algorithms of Phase I
6.9.3 Experimental Results (Phase II)
6.9.4 The Performance Evaluation of the Participating Algorithms of Phase II
6.10 Conclusion
6.11 Summary
Exercise Questions
Coding examples
Chapter 7 Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding
7.1 Introduction
7.2 Multi-objective Optimization
7.3 Experimental Methodology for Gray-Scale Multi-Level Image Thresholding
7.3.1 Quantum Inspired Non-dominated Sorting-Based Multi-objective Genetic Algorithm
7.3.2 Complexity Analysis
7.3.3 Quantum Inspired Simulated Annealing for Multi-objective Algorithms
7.3.3.1 Complexity Analysis
7.3.4 Quantum Inspired Multi-objective Particle Swarm Optimization
7.3.4.1 Complexity Analysis
7.3.5 Quantum Inspired Multi-objective Ant Colony Optimization
7.3.5.1 Complexity Analysis
7.4 Implementation Results
7.4.1 Experimental Results
7.4.1.1 The Results of Multi-Level Thresholding for QINSGA-II, NSGA-II, and SMS-EMOA
7.4.1.2 The Stability of the Comparable Methods
7.4.1.3 Performance Evaluation
7.5 Conclusion
7.6 Summary
Exercise Questions
Coding Examples
Chapter 8 Conclusion
Bibliography
Index
EULA

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