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E-raamat: Quantum Inspired Meta-heuristics for Image Analysis

(RCC Institute of Information Technology, Kolkata, India), (Jadavpur University, Kolkata, India), (Global Institute of Management and Technology, West Bengal, India)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 03-Jun-2019
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119488781
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Quantum Inspired Meta-heuristics for Image AnalysisSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 03-Jun-2019
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119488781
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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. 

Preface xiii
Acronyms xv
1 Introduction 1(28)
1.1 Image Analysis
3(4)
1.1.1 Image Segmentation
4(1)
1.1.2 Image Thresholding
5(2)
1.2 Prerequisites of Quantum Computing
7(9)
1.2.1 Dirac's Notation
8(1)
1.2.2 Qubit
8(1)
1.2.3 Quantum Superposition
8(1)
1.2.4 Quantum Gates
9(5)
1.2.4.1 Quantum NOT Gate (Matrix Representation)
9(1)
1.2.4.2 Quantum Z Gate (Matrix Representation)
9(1)
1.2.4.3 Hadamard Gate
10(1)
1.2.4.4 Phase Shift Gate
10(1)
1.2.4.5 Controlled NOT Gate (CNOT)
10(1)
1.2.4.6 SWAP Gate
11(1)
1.2.4.7 Toffoli Gate
11(1)
1.2.4.8 Fredkin Gate
12(1)
1.2.4.9 Quantum Rotation Gate
13(1)
1.2.5 Quantum Register
14(1)
1.2.6 Quantum Entanglement
14(1)
1.2.7 Quantum Solutions of NP-complete Problems
15(1)
1.3 Role of Optimization
16(3)
1.3.1 Single-objective Optimization
16(2)
1.3.2 Multi-objective Optimization
18(1)
1.3.3 Application of Optimization to Image Analysis
18(1)
1.4 Related Literature Survey
19(4)
1.4.1 Quantum-based Approaches
19(2)
1.4.2 Meta-heuristic-based Approaches
21(1)
1.4.3 Multi-objective-based Approaches
22(1)
1.5 Organization of the Book
23(2)
1.5.1 Quantum Inspired Meta-heuristics for Bi-level Image Thresholding
24(1)
1.5.2 Quantum Inspired Meta-heuristics for Gray-scale Multi-level Image Thresholding
24(1)
1.5.3 Quantum Behaved Meta-heuristics for True Color Multi-level Thresholding
24(1)
1.5.4 Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding
24(1)
1.6 Conclusion
25(1)
1.7 Summary
25(1)
Exercise Questions
26(3)
2 Review of Image Analysis 29(22)
2.1 Introduction
29(1)
2.2 Definition
29(1)
2.3 Mathematical Formalism
30(1)
2.4 Current Technologies
30(5)
2.4.1 Digital Image Analysis Methodologies
31(4)
2.4.1.1 Image Segmentation
31(1)
2.4.1.2 Feature Extraction/Selection
32(2)
2.4.1.3 Classification
34(1)
2.5 Overview of Different Thresholding Techniques
35(11)
2.5.1 Ramesh's Algorithm
35(1)
2.5.2 Shanbag's Algorithm
36(1)
2.5.3 Correlation Coefficient
37(1)
2.5.4 Pun's Algorithm
38(1)
2.5.5 Wu's Algorithm
38(1)
2.5.6 Renyi's Algorithm
39(1)
2.5.7 Yen's Algorithm
39(1)
2.5.8 Johannsen's Algorithm
40(1)
2.5.9 Silva's Algorithm
40(1)
2.5.10 Fuzzy Algorithm
41(1)
2.5.11 Brink's Algorithm
41(2)
2.5.12 Otsu's Algorithm
43(1)
2.5.13 Kittler's Algorithm
43(1)
2.5.14 Li's Algorithm
44(1)
2.5.15 Kapur's Algorithm
44(1)
2.5.16 Huang's Algorithm
45(1)
2.6 Applications of Image Analysis
46(1)
2.7 Conclusion
47(1)
2.8 Summary
48(1)
Exercise Questions
48(3)
3 Overview of Meta-heuristics 51(18)
3.1 Introduction
51(1)
3.1.1 Impact on Controlling Parameters
52(1)
3.2 Genetic Algorithms
52(4)
3.2.1 Fundamental Principles and Features
53(1)
3.2.2 Pseudo-code of Genetic Algorithms
53(1)
3.2.3 Encoding Strategy and the Creation of Population
54(1)
3.2.4 Evaluation Techniques
54(1)
3.2.5 Genetic Operators
54(1)
3.2.6 Selection Mechanism
54(1)
3.2.7 Crossover
55(1)
3.2.8 Mutation
56(1)
3.3 Particle Swarm Optimization
56(2)
3.3.1 Pseudo-code of Particle Swarm Optimization
57(1)
3.3.2 PSO: Velocity and Position Update
57(1)
3.4 Ant Colony Optimization
58(2)
3.4.1 Stigmergy in Ants: Biological Inspiration
58(1)
3.4.2 Pseudo-code of Ant Colony Optimization
59(1)
3.4.3 Pheromone Trails
59(1)
3.4.4 Updating Pheromone Trails
59(1)
3.5 Differential Evolution
60(2)
3.5.1 Pseudo-code of Differential Evolution
60(1)
3.5.2 Basic Principles of DE
61(1)
3.5.3 Mutation
61(1)
3.5.4 Crossover
61(1)
3.5.5 Selection
62(1)
3.6 Simulated Annealing
62(2)
3.6.1 Pseudo-code of Simulated Annealing
62(1)
3.6.2 Basics of Simulated Annealing
63(1)
3.7 Tabu Search
64(1)
3.7.1 Pseudo-code of Tabu Search
64(1)
3.7.2 Memory Management in Tabu Search
65(1)
3.7.3 Parameters Used in Tabu Search
65(1)
3.8 Conclusion
65(1)
3.9 Summary
65(1)
Exercise Questions
66(3)
4 Quantum Inspired Meta-heuristics for Bi-level Image Thresholding 69(56)
4.1 Introduction
69(1)
4.2 Quantum Inspired Genetic Algorithm
70(6)
4.2.1 Initialize the Population of Qubit Encoded Chromosomes
71(1)
4.2.2 Perform Quantum Interference
72(2)
4.2.2.1 Generate Random Chaotic Map for Each Qubit State
72(1)
4.2.2.2 Initiate Probabilistic Switching Between Chaotic Maps
73(1)
4.2.3 Find the Threshold Value in Population and Evaluate Fitness
74(1)
4.2.4 Apply Selection Mechanism to Generate a New Population
74(1)
4.2.5 Foundation of Quantum Crossover
74(1)
4.2.6 Foundation of Quantum Mutation
74(1)
4.2.7 Foundation of Quantum Shift
75(1)
4.2.8 Complexity Analysis
75(1)
4.3 Quantum Inspired Particle Swarm Optimization
76(1)
4.3.1 Complexity Analysis
77(1)
4.4 Implementation Results
77(43)
4.4.1 Experimental Results (Phase I)
79(17)
4.4.1.1 Implementation Results for QEA
91(5)
4.4.2 Experimental Results (Phase II)
96(18)
4.4.2.1 Experimental Results of Proposed QIGA and Conventional GA
96(1)
4.4.2.2 Results Obtained with QEA
96(18)
4.4.3 Experimental Results (Phase III)
114(12)
4.4.3.1 Results Obtained with Proposed QIGA and Conventional GA
114(3)
4.4.3.2 Results obtained from QEA
117(3)
4.5 Comparative Analysis among the Participating Algorithms
120(1)
4.6 Conclusion
120(1)
4.7 Summary
121(1)
Exercise Questions
121(2)
Coding Examples
123(2)
5 Quantum Inspired Meta-Heuristics for Gray-Scale Multi-Level Image Thresholding 125(70)
5.1 Introduction
125(1)
5.2 Quantum Inspired Genetic Algorithm
126(4)
5.2.1 Population Generation
126(1)
5.2.2 Quantum Orthogonality
127(1)
5.2.3 Determination of Threshold Values in Population and Measurement of Fitness
128(1)
5.2.4 Selection
129(1)
5.2.5 Quantum Crossover
129(1)
5.2.6 Quantum Mutation
129(1)
5.2.7 Complexity Analysis
129(1)
5.3 Quantum Inspired Particle Swarm Optimization
130(1)
5.3.1 Complexity Analysis
131(1)
5.4 Quantum Inspired Differential Evolution
131(2)
5.4.1 Complexity Analysis
132(1)
5.5 Quantum Inspired Ant Colony Optimization
133(1)
5.5.1 Complexity Analysis
133(1)
5.6 Quantum Inspired Simulated Annealing
134(2)
5.6.1 Complexity Analysis
136(1)
5.7 Quantum Inspired Tabu Search
136(1)
5.7.1 Complexity Analysis
136(1)
5.8 Implementation Results
137(8)
5.8.1 Consensus Results of the Quantum Algorithms
142(3)
5.9 Comparison of QIPSO with Other Existing Algorithms
145(20)
5.10 Conclusion
165(1)
5.11 Summary
166(1)
Exercise Questions
167(23)
Coding Examples
190(5)
6 Quantum Behaved Meta-Heuristics for True Color Multi-Level Image Thresholding 195(106)
6.1 Introduction
195(1)
6.2 Background
196(1)
6.3 Quantum Inspired Ant Colony Optimization
196(1)
6.3.1 Complexity Analysis
197(1)
6.4 Quantum Inspired Differential Evolution
197(3)
6.4.1 Complexity Analysis
200(1)
6.5 Quantum Inspired Particle Swarm Optimization
200(1)
6.5.1 Complexity Analysis
200(1)
6.6 Quantum Inspired Genetic Algorithm
201(2)
6.6.1 Complexity Analysis
203(1)
6.7 Quantum Inspired Simulated Annealing
203(1)
6.7.1 Complexity Analysis
204(1)
6.8 Quantum Inspired Tabu Search
204(3)
6.8.1 Complexity Analysis
206(1)
6.9 Implementation Results
207(87)
6.9.1 Experimental Results (Phase I)
209(16)
6.9.1.1 The Stability of the Comparable Algorithms
210(15)
6.9.2 The Performance Evaluation of the Comparable Algorithms of Phase I
225(10)
6.9.3 Experimental Results (Phase II)
235(1)
6.9.4 The Performance Evaluation of the Participating Algorithms of Phase II
235(59)
6.10 Conclusion
294(1)
6.11 Summary
294(1)
Exercise Questions
295(1)
Coding Examples
296(5)
7 Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding 301(32)
7.1 Introduction
301(1)
7.2 Multi-objective Optimization
302(1)
7.3 Experimental Methodology for Gray-Scale Multi-Level Image Thresholding
303(8)
7.3.1 Quantum Inspired Non-dominated Sorting-Based Multi-objective Genetic Algorithm
303(2)
7.3.2 Complexity Analysis
305(1)
7.3.3 Quantum Inspired Simulated Annealing for Multi-objective Algorithms
305(3)
7.3.3.1 Complexity Analysis
307(1)
7.3.4 Quantum Inspired Multi-objective Particle Swarm Optimization
308(1)
7.3.4.1 Complexity Analysis
309(1)
7.3.5 Quantum Inspired Multi-objective Ant Colony Optimization
309(2)
7.3.5.1 Complexity Analysis
310(1)
7.4 Implementation Results
311(16)
7.4.1 Experimental Results
311(16)
7.4.1.1 The Results of Multi-Level Thresholding for QINSGA-II, NSGA-II, and SMS-EMOA
312(1)
7.4.1.2 The Stability of the Comparable Methods
312(3)
7.4.1.3 Performance Evaluation
315(12)
7.5 Conclusion
327(1)
7.6 Summary
327(1)
Exercise Questions
328(1)
Coding Examples
329(4)
8 Conclusion 333(4)
Bibliography 337(18)
Index 355
SANDIP DEY, PHD, 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.

SIDDHARTHA BHATTACHARYYA, PHD, is the Principal of RCC Institute of Information Technology, Kolkata, India.

UJJWAL MAULIK, PHD, is the Chair of and Professor in the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
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