Details

<p>Preface xiii</p> <p>Acronyms xv</p> <p><b>1 Introduction </b><b>1</b></p> <p>1.1 Image Analysis 3</p> <p>1.1.1 Image Segmentation 4</p> <p>1.1.2 Image Thresholding 5</p> <p>1.2 Prerequisites of Quantum Computing 7</p> <p>1.2.1 Dirac’s Notation 8</p> <p>1.2.2 Qubit 8</p> <p>1.2.3 Quantum Superposition 8</p> <p>1.2.4 Quantum Gates 9</p> <p>1.2.4.1 Quantum NOT Gate (Matrix Representation) 9</p> <p>1.2.4.2 Quantum Z Gate (Matrix Representation) 9</p> <p>1.2.4.3 Hadamard Gate 10</p> <p>1.2.4.4 Phase Shift Gate 10</p> <p>1.2.4.5 Controlled NOT Gate (CNOT) 10</p> <p>1.2.4.6 SWAP Gate 11</p> <p>1.2.4.7 Toffoli Gate 11</p> <p>1.2.4.8 Fredkin Gate 12</p> <p>1.2.4.9 Quantum Rotation Gate 13</p> <p>1.2.5 Quantum Register 14</p> <p>1.2.6 Quantum Entanglement 14</p> <p>1.2.7 Quantum Solutions of NP-complete Problems 15</p> <p>1.3 Role of Optimization 16</p> <p>1.3.1 Single-objective Optimization 16</p> <p>1.3.2 Multi-objective Optimization 18</p> <p>1.3.3 Application of Optimization to Image Analysis 18</p> <p>1.4 Related Literature Survey 19</p> <p>1.4.1 Quantum-based Approaches 19</p> <p>1.4.2 Meta-heuristic-based Approaches 21</p> <p>1.4.3 Multi-objective-based Approaches 22</p> <p>1.5 Organization of the Book 23</p> <p>1.5.1 Quantum Inspired Meta-heuristics for Bi-level Image Thresholding 24</p> <p>1.5.2 Quantum Inspired Meta-heuristics for Gray-scale Multi-level Image Thresholding 24</p> <p>1.5.3 Quantum Behaved Meta-heuristics for True Color Multi-level Thresholding 24</p> <p>1.5.4 Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding 24</p> <p>1.6 Conclusion 25</p> <p>1.7 Summary 25</p> <p>Exercise Questions 26</p> <p><b>2 Review of Image Analysis </b><b>29</b></p> <p>2.1 Introduction 29</p> <p>2.2 Definition 29</p> <p>2.3 Mathematical Formalism 30</p> <p>2.4 Current Technologies 30</p> <p>2.4.1 Digital Image Analysis Methodologies 31</p> <p>2.4.1.1 Image Segmentation 31</p> <p>2.4.1.2 Feature Extraction/Selection 32</p> <p>2.4.1.3 Classification 34</p> <p>2.5 Overview of Different Thresholding Techniques 35</p> <p>2.5.1 Ramesh’s Algorithm 35</p> <p>2.5.2 Shanbag’s Algorithm 36</p> <p>2.5.3 Correlation Coefficient 37</p> <p>2.5.4 Pun’s Algorithm 38</p> <p>2.5.5 Wu’s Algorithm 38</p> <p>2.5.6 Renyi’s Algorithm 39</p> <p>2.5.7 Yen’s Algorithm 39</p> <p>2.5.8 Johannsen’s Algorithm 40</p> <p>2.5.9 Silva’s Algorithm 40</p> <p>2.5.10 Fuzzy Algorithm 41</p> <p>2.5.11 Brink’s Algorithm 41</p> <p>2.5.12 Otsu’s Algorithm 43</p> <p>2.5.13 Kittler’s Algorithm 43</p> <p>2.5.14 Li’s Algorithm 44</p> <p>2.5.15 Kapur’s Algorithm 44</p> <p>2.5.16 Huang’s Algorithm 45</p> <p>2.6 Applications of Image Analysis 46</p> <p>2.7 Conclusion 47</p> <p>2.8 Summary 48</p> <p>Exercise Questions 48</p> <p><b>3 Overview of Meta-heuristics </b><b>51</b></p> <p>3.1 Introduction 51</p> <p>3.1.1 Impact on Controlling Parameters 52</p> <p>3.2 Genetic Algorithms 52</p> <p>3.2.1 Fundamental Principles and Features 53</p> <p>3.2.2 Pseudo-code of Genetic Algorithms 53</p> <p>3.2.3 Encoding Strategy and the Creation of Population 54</p> <p>3.2.4 Evaluation Techniques 54</p> <p>3.2.5 Genetic Operators 54</p> <p>3.2.6 Selection Mechanism 54</p> <p>3.2.7 Crossover 55</p> <p>3.2.8 Mutation 56</p> <p>3.3 Particle Swarm Optimization 56</p> <p>3.3.1 Pseudo-code of Particle Swarm Optimization 57</p> <p>3.3.2 PSO: Velocity and Position Update 57</p> <p>3.4 Ant Colony Optimization 58</p> <p>3.4.1 Stigmergy in Ants: Biological Inspiration 58</p> <p>3.4.2 Pseudo-code of Ant Colony Optimization 59</p> <p>3.4.3 Pheromone Trails 59</p> <p>3.4.4 Updating Pheromone Trails 59</p> <p>3.5 Differential Evolution 60</p> <p>3.5.1 Pseudo-code of Differential Evolution 60</p> <p>3.5.2 Basic Principles of DE 61</p> <p>3.5.3 Mutation 61</p> <p>3.5.4 Crossover 61</p> <p>3.5.5 Selection 62</p> <p>3.6 Simulated Annealing 62</p> <p>3.6.1 Pseudo-code of Simulated Annealing 62</p> <p>3.6.2 Basics of Simulated Annealing 63</p> <p>3.7 Tabu Search 64</p> <p>3.7.1 Pseudo-code of Tabu Search 64</p> <p>3.7.2 Memory Management in Tabu Search 65</p> <p>3.7.3 Parameters Used in Tabu Search 65</p> <p>3.8 Conclusion 65</p> <p>3.9 Summary 65</p> <p>Exercise Questions 66</p> <p><b>4 Quantum Inspired Meta-heuristics for Bi-level Image Thresholding </b><b>69</b></p> <p>4.1 Introduction 69</p> <p>4.2 Quantum Inspired Genetic Algorithm 70</p> <p>4.2.1 Initialize the Population of Qubit Encoded Chromosomes 71</p> <p>4.2.2 Perform Quantum Interference 72</p> <p>4.2.2.1 Generate Random Chaotic Map for Each Qubit State 72</p> <p>4.2.2.2 Initiate Probabilistic Switching Between Chaotic Maps 73</p> <p>4.2.3 Find the Threshold Value in Population and Evaluate Fitness 74</p> <p>4.2.4 Apply Selection Mechanism to Generate a New Population 74</p> <p>4.2.5 Foundation of Quantum Crossover 74</p> <p>4.2.6 Foundation of Quantum Mutation 74</p> <p>4.2.7 Foundation of Quantum Shift 75</p> <p>4.2.8 Complexity Analysis 75</p> <p>4.3 Quantum Inspired Particle Swarm Optimization 76</p> <p>4.3.1 Complexity Analysis 77</p> <p>4.4 Implementation Results 77</p> <p>4.4.1 Experimental Results (Phase I) 79</p> <p>4.4.1.1 Implementation Results for QEA 91</p> <p>4.4.2 Experimental Results (Phase II) 96</p> <p>4.4.2.1 Experimental Results of Proposed QIGA and Conventional GA 96</p> <p>4.4.2.2 Results Obtained with QEA 96</p> <p>4.4.3 Experimental Results (Phase III) 114</p> <p>4.4.3.1 Results Obtained with Proposed QIGA and Conventional GA 114</p> <p>4.4.3.2 Results obtained from QEA 117</p> <p>4.5 Comparative Analysis among the Participating Algorithms 120</p> <p>4.6 Conclusion 120</p> <p>4.7 Summary 121</p> <p>Exercise Questions 121</p> <p>Coding Examples 123</p> <p><b>5 Quantum Inspired Meta-Heuristics for Gray-Scale Multi-Level Image Thresholding </b><b>125</b></p> <p>5.1 Introduction 125</p> <p>5.2 Quantum Inspired Genetic Algorithm 126</p> <p>5.2.1 Population Generation 126</p> <p>5.2.2 Quantum Orthogonality 127</p> <p>5.2.3 Determination of Threshold Values in Population and Measurement of Fitness 128</p> <p>5.2.4 Selection 129</p> <p>5.2.5 Quantum Crossover 129</p> <p>5.2.6 Quantum Mutation 129</p> <p>5.2.7 Complexity Analysis 129</p> <p>5.3 Quantum Inspired Particle Swarm Optimization 130</p> <p>5.3.1 Complexity Analysis 131</p> <p>5.4 Quantum Inspired Differential Evolution 131</p> <p>5.4.1 Complexity Analysis 132</p> <p>5.5 Quantum Inspired Ant Colony Optimization 133</p> <p>5.5.1 Complexity Analysis 133</p> <p>5.6 Quantum Inspired Simulated Annealing 134</p> <p>5.6.1 Complexity Analysis 136</p> <p>5.7 Quantum Inspired Tabu Search 136</p> <p>5.7.1 Complexity Analysis 136</p> <p>5.8 Implementation Results 137</p> <p>5.8.1 Consensus Results of the Quantum Algorithms 142</p> <p>5.9 Comparison of QIPSO with Other Existing Algorithms 145</p> <p>5.10 Conclusion 165</p> <p>5.11 Summary 166</p> <p>Exercise Questions 167</p> <p>Coding Examples 190</p> <p><b>6 Quantum Behaved Meta-Heuristics for True Color Multi-Level Image Thresholding </b><b>195</b></p> <p>6.1 Introduction 195</p> <p>6.2 Background 196</p> <p>6.3 Quantum Inspired Ant Colony Optimization 196</p> <p>6.3.1 Complexity Analysis 197</p> <p>6.4 Quantum Inspired Differential Evolution 197</p> <p>6.4.1 Complexity Analysis 200</p> <p>6.5 Quantum Inspired Particle Swarm Optimization 200</p> <p>6.5.1 Complexity Analysis 200</p> <p>6.6 Quantum Inspired Genetic Algorithm 201</p> <p>6.6.1 Complexity Analysis 203</p> <p>6.7 Quantum Inspired Simulated Annealing 203</p> <p>6.7.1 Complexity Analysis 204</p> <p>6.8 Quantum Inspired Tabu Search 204</p> <p>6.8.1 Complexity Analysis 206</p> <p>6.9 Implementation Results 207</p> <p>6.9.1 Experimental Results (Phase I) 209</p> <p>6.9.1.1 The Stability of the Comparable Algorithms 210</p> <p>6.9.2 The Performance Evaluation of the Comparable Algorithms of Phase I 225</p> <p>6.9.3 Experimental Results (Phase II) 235</p> <p>6.9.4 The Performance Evaluation of the Participating Algorithms of Phase II 235</p> <p>6.10 Conclusion 294</p> <p>6.11 Summary 294</p> <p>Exercise Questions 295</p> <p>Coding Examples 296</p> <p><b>7 Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding </b><b>301</b></p> <p>7.1 Introduction 301</p> <p>7.2 Multi-objective Optimization 302</p> <p>7.3 Experimental Methodology for Gray-Scale Multi-Level Image Thresholding 303</p> <p>7.3.1 Quantum Inspired Non-dominated Sorting-Based Multi-objective Genetic Algorithm 303</p> <p>7.3.2 Complexity Analysis 305</p> <p>7.3.3 Quantum Inspired Simulated Annealing for Multi-objective Algorithms 305</p> <p>7.3.3.1 Complexity Analysis 307</p> <p>7.3.4 Quantum Inspired Multi-objective Particle Swarm Optimization 308</p> <p>7.3.4.1 Complexity Analysis 309</p> <p>7.3.5 Quantum Inspired Multi-objective Ant Colony Optimization 309</p> <p>7.3.5.1 Complexity Analysis 310</p> <p>7.4 Implementation Results 311</p> <p>7.4.1 Experimental Results 311</p> <p>7.4.1.1 The Results of Multi-Level Thresholding for QINSGA-II, NSGA-II, and SMS-EMOA 312</p> <p>7.4.1.2 The Stability of the Comparable Methods 312</p> <p>7.4.1.3 Performance Evaluation 315</p> <p>7.5 Conclusion 327</p> <p>7.6 Summary 327</p> <p>Exercise Questions 328</p> <p>Coding Examples 329</p> <p><b>8 Conclusion </b><b>333</b></p> <p>Bibliography 337</p> <p>Index 355</p>

<p><b>SANDIP DEY, P<small>H</small>D,</b> 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. <p><b>SIDDHARTHA BHATTACHARYYA, P<small>H</small>D,</b> is the Principal of RCC Institute of Information Technology, Kolkata, India. <p><b>UJJWAL MAULIK, P<small>H</small>D,</b> is the Chair of and Professor in the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.

<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>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><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. <ul> <li>Provides an in-depth analysis of quantum mechanical principles</li> <li>Offers a comprehensive review of image analysis</li> <li>Analyzes different state-of-the-art image thresholding approaches</li> <li>Details 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 counterparts</li> </ul> <p><i>Quantum Inspired Meta-heuristics for Image Analysis</i>???is an excellent source of information for anyone working with or learning quantum inspired meta-heuristics for image analysis.

GB