{"product_id":"quantum-inspired-metaheuristics-for-image-analysis-9781119488750","title":"Quantum Inspired Metaheuristics for Image","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eIntroduces quantum inspired techniques for image analysis for pure and true gray scale\/color images in a single\/multi-objective environment\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eQuantum Inspired Meta-heuristics for Image Analysis\u003c\/i\u003e 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\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcronyms xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Image Analysis 3\u003c\/p\u003e \u003cp\u003e1.1.1 Image Segmentation 4\u003c\/p\u003e \u003cp\u003e1.1.2 Image Thresholding 5\u003c\/p\u003e \u003cp\u003e1.2 Prerequisites of Quantum Computing 7\u003c\/p\u003e \u003cp\u003e1.2.1 Dirac’s Notation 8\u003c\/p\u003e \u003cp\u003e1.2.2 Qubit 8\u003c\/p\u003e \u003cp\u003e1.2.3 Quantum Superposition 8\u003c\/p\u003e \u003cp\u003e1.2.4 Quantum Gates 9\u003c\/p\u003e \u003cp\u003e1.2.4.1 Quantum NOT Gate (Matrix Representation) 9\u003c\/p\u003e \u003cp\u003e1.2.4.2 Quantum Z Gate (Matrix Representation) 9\u003c\/p\u003e \u003cp\u003e1.2.4.3 Hadamard Gate 10\u003c\/p\u003e \u003cp\u003e1.2.4.4 Phase Shift Gate 10\u003c\/p\u003e \u003cp\u003e1.2.4.5 Controlled NOT Gate (CNOT) 10\u003c\/p\u003e \u003cp\u003e1.2.4.6 SWAP Gate 11\u003c\/p\u003e \u003cp\u003e1.2.4.7 Toffoli Gate 11\u003c\/p\u003e \u003cp\u003e1.2.4.8 Fredkin Gate 12\u003c\/p\u003e \u003cp\u003e1.2.4.9 Quantum Rotation Gate 13\u003c\/p\u003e \u003cp\u003e1.2.5 Quantum Register 14\u003c\/p\u003e \u003cp\u003e1.2.6 Quantum Entanglement 14\u003c\/p\u003e \u003cp\u003e1.2.7 Quantum Solutions of NP-complete Problems 15\u003c\/p\u003e \u003cp\u003e1.3 Role of Optimization 16\u003c\/p\u003e \u003cp\u003e1.3.1 Single-objective Optimization 16\u003c\/p\u003e \u003cp\u003e1.3.2 Multi-objective Optimization 18\u003c\/p\u003e \u003cp\u003e1.3.3 Application of Optimization to Image Analysis 18\u003c\/p\u003e \u003cp\u003e1.4 Related Literature Survey 19\u003c\/p\u003e \u003cp\u003e1.4.1 Quantum-based Approaches 19\u003c\/p\u003e \u003cp\u003e1.4.2 Meta-heuristic-based Approaches 21\u003c\/p\u003e \u003cp\u003e1.4.3 Multi-objective-based Approaches 22\u003c\/p\u003e \u003cp\u003e1.5 Organization of the Book 23\u003c\/p\u003e \u003cp\u003e1.5.1 Quantum Inspired Meta-heuristics for Bi-level Image Thresholding 24\u003c\/p\u003e \u003cp\u003e1.5.2 Quantum Inspired Meta-heuristics for Gray-scale Multi-level Image Thresholding 24\u003c\/p\u003e \u003cp\u003e1.5.3 Quantum Behaved Meta-heuristics for True Color Multi-level Thresholding 24\u003c\/p\u003e \u003cp\u003e1.5.4 Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding 24\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 25\u003c\/p\u003e \u003cp\u003e1.7 Summary 25\u003c\/p\u003e \u003cp\u003eExercise Questions 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Review of Image Analysis \u003c\/b\u003e\u003cb\u003e29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 29\u003c\/p\u003e \u003cp\u003e2.2 Definition 29\u003c\/p\u003e \u003cp\u003e2.3 Mathematical Formalism 30\u003c\/p\u003e \u003cp\u003e2.4 Current Technologies 30\u003c\/p\u003e \u003cp\u003e2.4.1 Digital Image Analysis Methodologies 31\u003c\/p\u003e \u003cp\u003e2.4.1.1 Image Segmentation 31\u003c\/p\u003e \u003cp\u003e2.4.1.2 Feature Extraction\/Selection 32\u003c\/p\u003e \u003cp\u003e2.4.1.3 Classification 34\u003c\/p\u003e \u003cp\u003e2.5 Overview of Different Thresholding Techniques 35\u003c\/p\u003e \u003cp\u003e2.5.1 Ramesh’s Algorithm 35\u003c\/p\u003e \u003cp\u003e2.5.2 Shanbag’s Algorithm 36\u003c\/p\u003e \u003cp\u003e2.5.3 Correlation Coefficient 37\u003c\/p\u003e \u003cp\u003e2.5.4 Pun’s Algorithm 38\u003c\/p\u003e \u003cp\u003e2.5.5 Wu’s Algorithm 38\u003c\/p\u003e \u003cp\u003e2.5.6 Renyi’s Algorithm 39\u003c\/p\u003e \u003cp\u003e2.5.7 Yen’s Algorithm 39\u003c\/p\u003e \u003cp\u003e2.5.8 Johannsen’s Algorithm 40\u003c\/p\u003e \u003cp\u003e2.5.9 Silva’s Algorithm 40\u003c\/p\u003e \u003cp\u003e2.5.10 Fuzzy Algorithm 41\u003c\/p\u003e \u003cp\u003e2.5.11 Brink’s Algorithm 41\u003c\/p\u003e \u003cp\u003e2.5.12 Otsu’s Algorithm 43\u003c\/p\u003e \u003cp\u003e2.5.13 Kittler’s Algorithm 43\u003c\/p\u003e \u003cp\u003e2.5.14 Li’s Algorithm 44\u003c\/p\u003e \u003cp\u003e2.5.15 Kapur’s Algorithm 44\u003c\/p\u003e \u003cp\u003e2.5.16 Huang’s Algorithm 45\u003c\/p\u003e \u003cp\u003e2.6 Applications of Image Analysis 46\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 47\u003c\/p\u003e \u003cp\u003e2.8 Summary 48\u003c\/p\u003e \u003cp\u003eExercise Questions 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Overview of Meta-heuristics \u003c\/b\u003e\u003cb\u003e51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 51\u003c\/p\u003e \u003cp\u003e3.1.1 Impact on Controlling Parameters 52\u003c\/p\u003e \u003cp\u003e3.2 Genetic Algorithms 52\u003c\/p\u003e \u003cp\u003e3.2.1 Fundamental Principles and Features 53\u003c\/p\u003e \u003cp\u003e3.2.2 Pseudo-code of Genetic Algorithms 53\u003c\/p\u003e \u003cp\u003e3.2.3 Encoding Strategy and the Creation of Population 54\u003c\/p\u003e \u003cp\u003e3.2.4 Evaluation Techniques 54\u003c\/p\u003e \u003cp\u003e3.2.5 Genetic Operators 54\u003c\/p\u003e \u003cp\u003e3.2.6 Selection Mechanism 54\u003c\/p\u003e \u003cp\u003e3.2.7 Crossover 55\u003c\/p\u003e \u003cp\u003e3.2.8 Mutation 56\u003c\/p\u003e \u003cp\u003e3.3 Particle Swarm Optimization 56\u003c\/p\u003e \u003cp\u003e3.3.1 Pseudo-code of Particle Swarm Optimization 57\u003c\/p\u003e \u003cp\u003e3.3.2 PSO: Velocity and Position Update 57\u003c\/p\u003e \u003cp\u003e3.4 Ant Colony Optimization 58\u003c\/p\u003e \u003cp\u003e3.4.1 Stigmergy in Ants: Biological Inspiration 58\u003c\/p\u003e \u003cp\u003e3.4.2 Pseudo-code of Ant Colony Optimization 59\u003c\/p\u003e \u003cp\u003e3.4.3 Pheromone Trails 59\u003c\/p\u003e \u003cp\u003e3.4.4 Updating Pheromone Trails 59\u003c\/p\u003e \u003cp\u003e3.5 Differential Evolution 60\u003c\/p\u003e \u003cp\u003e3.5.1 Pseudo-code of Differential Evolution 60\u003c\/p\u003e \u003cp\u003e3.5.2 Basic Principles of DE 61\u003c\/p\u003e \u003cp\u003e3.5.3 Mutation 61\u003c\/p\u003e \u003cp\u003e3.5.4 Crossover 61\u003c\/p\u003e \u003cp\u003e3.5.5 Selection 62\u003c\/p\u003e \u003cp\u003e3.6 Simulated Annealing 62\u003c\/p\u003e \u003cp\u003e3.6.1 Pseudo-code of Simulated Annealing 62\u003c\/p\u003e \u003cp\u003e3.6.2 Basics of Simulated Annealing 63\u003c\/p\u003e \u003cp\u003e3.7 Tabu Search 64\u003c\/p\u003e \u003cp\u003e3.7.1 Pseudo-code of Tabu Search 64\u003c\/p\u003e \u003cp\u003e3.7.2 Memory Management in Tabu Search 65\u003c\/p\u003e \u003cp\u003e3.7.3 Parameters Used in Tabu Search 65\u003c\/p\u003e \u003cp\u003e3.8 Conclusion 65\u003c\/p\u003e \u003cp\u003e3.9 Summary 65\u003c\/p\u003e \u003cp\u003eExercise Questions 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Quantum Inspired Meta-heuristics for Bi-level Image Thresholding \u003c\/b\u003e\u003cb\u003e69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 69\u003c\/p\u003e \u003cp\u003e4.2 Quantum Inspired Genetic Algorithm 70\u003c\/p\u003e \u003cp\u003e4.2.1 Initialize the Population of Qubit Encoded Chromosomes 71\u003c\/p\u003e \u003cp\u003e4.2.2 Perform Quantum Interference 72\u003c\/p\u003e \u003cp\u003e4.2.2.1 Generate Random Chaotic Map for Each Qubit State 72\u003c\/p\u003e \u003cp\u003e4.2.2.2 Initiate Probabilistic Switching Between Chaotic Maps 73\u003c\/p\u003e \u003cp\u003e4.2.3 Find the Threshold Value in Population and Evaluate Fitness 74\u003c\/p\u003e \u003cp\u003e4.2.4 Apply Selection Mechanism to Generate a New Population 74\u003c\/p\u003e \u003cp\u003e4.2.5 Foundation of Quantum Crossover 74\u003c\/p\u003e \u003cp\u003e4.2.6 Foundation of Quantum Mutation 74\u003c\/p\u003e \u003cp\u003e4.2.7 Foundation of Quantum Shift 75\u003c\/p\u003e \u003cp\u003e4.2.8 Complexity Analysis 75\u003c\/p\u003e \u003cp\u003e4.3 Quantum Inspired Particle Swarm Optimization 76\u003c\/p\u003e \u003cp\u003e4.3.1 Complexity Analysis 77\u003c\/p\u003e \u003cp\u003e4.4 Implementation Results 77\u003c\/p\u003e \u003cp\u003e4.4.1 Experimental Results (Phase I) 79\u003c\/p\u003e \u003cp\u003e4.4.1.1 Implementation Results for QEA 91\u003c\/p\u003e \u003cp\u003e4.4.2 Experimental Results (Phase II) 96\u003c\/p\u003e \u003cp\u003e4.4.2.1 Experimental Results of Proposed QIGA and Conventional GA 96\u003c\/p\u003e \u003cp\u003e4.4.2.2 Results Obtained with QEA 96\u003c\/p\u003e \u003cp\u003e4.4.3 Experimental Results (Phase III) 114\u003c\/p\u003e \u003cp\u003e4.4.3.1 Results Obtained with Proposed QIGA and Conventional GA 114\u003c\/p\u003e \u003cp\u003e4.4.3.2 Results obtained from QEA 117\u003c\/p\u003e \u003cp\u003e4.5 Comparative Analysis among the Participating Algorithms 120\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 120\u003c\/p\u003e \u003cp\u003e4.7 Summary 121\u003c\/p\u003e \u003cp\u003eExercise Questions 121\u003c\/p\u003e \u003cp\u003eCoding Examples 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Quantum Inspired Meta-Heuristics for Gray-Scale Multi-Level Image Thresholding \u003c\/b\u003e\u003cb\u003e125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 125\u003c\/p\u003e \u003cp\u003e5.2 Quantum Inspired Genetic Algorithm 126\u003c\/p\u003e \u003cp\u003e5.2.1 Population Generation 126\u003c\/p\u003e \u003cp\u003e5.2.2 Quantum Orthogonality 127\u003c\/p\u003e \u003cp\u003e5.2.3 Determination of Threshold Values in Population and Measurement of Fitness 128\u003c\/p\u003e \u003cp\u003e5.2.4 Selection 129\u003c\/p\u003e \u003cp\u003e5.2.5 Quantum Crossover 129\u003c\/p\u003e \u003cp\u003e5.2.6 Quantum Mutation 129\u003c\/p\u003e \u003cp\u003e5.2.7 Complexity Analysis 129\u003c\/p\u003e \u003cp\u003e5.3 Quantum Inspired Particle Swarm Optimization 130\u003c\/p\u003e \u003cp\u003e5.3.1 Complexity Analysis 131\u003c\/p\u003e \u003cp\u003e5.4 Quantum Inspired Differential Evolution 131\u003c\/p\u003e \u003cp\u003e5.4.1 Complexity Analysis 132\u003c\/p\u003e \u003cp\u003e5.5 Quantum Inspired Ant Colony Optimization 133\u003c\/p\u003e \u003cp\u003e5.5.1 Complexity Analysis 133\u003c\/p\u003e \u003cp\u003e5.6 Quantum Inspired Simulated Annealing 134\u003c\/p\u003e \u003cp\u003e5.6.1 Complexity Analysis 136\u003c\/p\u003e \u003cp\u003e5.7 Quantum Inspired Tabu Search 136\u003c\/p\u003e \u003cp\u003e5.7.1 Complexity Analysis 136\u003c\/p\u003e \u003cp\u003e5.8 Implementation Results 137\u003c\/p\u003e \u003cp\u003e5.8.1 Consensus Results of the Quantum Algorithms 142\u003c\/p\u003e \u003cp\u003e5.9 Comparison of QIPSO with Other Existing Algorithms 145\u003c\/p\u003e \u003cp\u003e5.10 Conclusion 165\u003c\/p\u003e \u003cp\u003e5.11 Summary 166\u003c\/p\u003e \u003cp\u003eExercise Questions 167\u003c\/p\u003e \u003cp\u003eCoding Examples 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Quantum Behaved Meta-Heuristics for True Color Multi-Level Image Thresholding \u003c\/b\u003e\u003cb\u003e195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 195\u003c\/p\u003e \u003cp\u003e6.2 Background 196\u003c\/p\u003e \u003cp\u003e6.3 Quantum Inspired Ant Colony Optimization 196\u003c\/p\u003e \u003cp\u003e6.3.1 Complexity Analysis 197\u003c\/p\u003e \u003cp\u003e6.4 Quantum Inspired Differential Evolution 197\u003c\/p\u003e \u003cp\u003e6.4.1 Complexity Analysis 200\u003c\/p\u003e \u003cp\u003e6.5 Quantum Inspired Particle Swarm Optimization 200\u003c\/p\u003e \u003cp\u003e6.5.1 Complexity Analysis 200\u003c\/p\u003e \u003cp\u003e6.6 Quantum Inspired Genetic Algorithm 201\u003c\/p\u003e \u003cp\u003e6.6.1 Complexity Analysis 203\u003c\/p\u003e \u003cp\u003e6.7 Quantum Inspired Simulated Annealing 203\u003c\/p\u003e \u003cp\u003e6.7.1 Complexity Analysis 204\u003c\/p\u003e \u003cp\u003e6.8 Quantum Inspired Tabu Search 204\u003c\/p\u003e \u003cp\u003e6.8.1 Complexity Analysis 206\u003c\/p\u003e \u003cp\u003e6.9 Implementation Results 207\u003c\/p\u003e \u003cp\u003e6.9.1 Experimental Results (Phase I) 209\u003c\/p\u003e \u003cp\u003e6.9.1.1 The Stability of the Comparable Algorithms 210\u003c\/p\u003e \u003cp\u003e6.9.2 The Performance Evaluation of the Comparable Algorithms of Phase I 225\u003c\/p\u003e \u003cp\u003e6.9.3 Experimental Results (Phase II) 235\u003c\/p\u003e \u003cp\u003e6.9.4 The Performance Evaluation of the Participating Algorithms of Phase II 235\u003c\/p\u003e \u003cp\u003e6.10 Conclusion 294\u003c\/p\u003e \u003cp\u003e6.11 Summary 294\u003c\/p\u003e \u003cp\u003eExercise Questions 295\u003c\/p\u003e \u003cp\u003eCoding Examples 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding \u003c\/b\u003e\u003cb\u003e301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 301\u003c\/p\u003e \u003cp\u003e7.2 Multi-objective Optimization 302\u003c\/p\u003e \u003cp\u003e7.3 Experimental Methodology for Gray-Scale Multi-Level Image Thresholding 303\u003c\/p\u003e \u003cp\u003e7.3.1 Quantum Inspired Non-dominated Sorting-Based Multi-objective Genetic Algorithm 303\u003c\/p\u003e \u003cp\u003e7.3.2 Complexity Analysis 305\u003c\/p\u003e \u003cp\u003e7.3.3 Quantum Inspired Simulated Annealing for Multi-objective Algorithms 305\u003c\/p\u003e \u003cp\u003e7.3.3.1 Complexity Analysis 307\u003c\/p\u003e \u003cp\u003e7.3.4 Quantum Inspired Multi-objective Particle Swarm Optimization 308\u003c\/p\u003e \u003cp\u003e7.3.4.1 Complexity Analysis 309\u003c\/p\u003e \u003cp\u003e7.3.5 Quantum Inspired Multi-objective Ant Colony Optimization 309\u003c\/p\u003e \u003cp\u003e7.3.5.1 Complexity Analysis 310\u003c\/p\u003e \u003cp\u003e7.4 Implementation Results 311\u003c\/p\u003e \u003cp\u003e7.4.1 Experimental Results 311\u003c\/p\u003e \u003cp\u003e7.4.1.1 The Results of Multi-Level Thresholding for QINSGA-II, NSGA-II, and SMS-EMOA 312\u003c\/p\u003e \u003cp\u003e7.4.1.2 The Stability of the Comparable Methods 312\u003c\/p\u003e \u003cp\u003e7.4.1.3 Performance Evaluation 315\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 327\u003c\/p\u003e \u003cp\u003e7.6 Summary 327\u003c\/p\u003e \u003cp\u003eExercise Questions 328\u003c\/p\u003e \u003cp\u003eCoding Examples 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Conclusion \u003c\/b\u003e\u003cb\u003e333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 337\u003c\/p\u003e \u003cp\u003eIndex 355\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407065162071,"sku":"9781119488750","price":107.96,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/quantum-inspired-metaheuristics-for-image-analysis-9781119488750","provider":"Book Curl","version":"1.0","type":"link"}