Quantum inspired meta-heuristics for image analysis

Quantum inspired meta-heuristics for image analysis /

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Bibliographic Details
Main Authors: Dey, Sandip, 1977- (Author), Bhattacharyya, Siddhartha, 1975- (Author), Maulik, Ujjwal (Author)
Corporate Author: ProQuest (Firm)
Format: Electronic eBook
Language:English
Published: Hoboken, NJ : John Wiley & Sons, Inc., 2019.
Subjects:
Image segmentation.
Image analysis.
Metaheuristics.
Heuristic algorithms.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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100 1 |a Dey, Sandip,  |d 1977-  |e author. 
245 1 0 |a Quantum inspired meta-heuristics for image analysis /  |c Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik. 
264 1 |a Hoboken, NJ :  |b John Wiley & Sons, Inc.,  |c 2019. 
264 4 |c ©2019 
300 |a 1 online resource (xvi, 358 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 0 |a Machine generated contents note:   |g 1.1.  |t Image Analysis --   |g 1.1.1.  |t Image Segmentation --   |g 1.1.2.  |t Image Thresholding --   |g 1.2.  |t Prerequisites of Quantum Computing --   |g 1.2.1.  |t Dirac's Notation --   |g 1.2.2.  |t Qubit --   |g 1.2.3.  |t Quantum Superposition --   |g 1.2.4.  |t Quantum Gates --   |g 1.2.4.1.  |t Quantum NOT Gate (Matrix Representation) --   |g 1.2.4.2.  |t Quantum Z Gate (Matrix Representation) --   |g 1.2.4.3.  |t Hadamard Gate --   |g 1.2.4.4.  |t Phase Shift Gate --   |g 1.2.4.5.  |t Controlled NOT Gate (CNOT) --   |g 1.2.4.6.  |t SWAP Gate --   |g 1.2.4.7.  |t Toffoli Gate --   |g 1.2.4.8.  |t Fredkin Gate --   |g 1.2.4.9.  |t Quantum Rotation Gate --   |g 1.2.5.  |t Quantum Register --   |g 1.2.6.  |t Quantum Entanglement --   |g 1.2.7.  |t Quantum Solutions of NP-complete Problems --   |g 1.3.  |t Role of Optimization --   |g 1.3.1.  |t Single-objective Optimization --   |g 1.3.2.  |t Multi-objective Optimization --   |g 1.3.3.  |t Application of Optimization to Image Analysis --   |g 1.4.  |t Related Literature Survey --   |g 1.4.1.  |t Quantum-based Approaches --   |g 1.4.2.  |t Meta-heuristic-based Approaches --   |g 1.4.3.  |t Multi-objective-based Approaches --   |g 1.5.  |t Organization of the Book --   |g 1.5.1.  |t Quantum Inspired Meta-heuristics for Bi-level Image Thresholding --   |g 1.5.2.  |t Quantum Inspired Meta-heuristics for Gray-scale Multi-level Image Thresholding --   |g 1.5.3.  |t Quantum Behaved Meta-heuristics for True Color Multi-level Thresholding --   |g 1.5.4.  |t Quantum Inspired Multi-objective Algorithms for Multi-level Image Thresholding --   |g 1.6.  |t Conclusion --   |g 1.7.  |t Summary --   |t Exercise Questions --   |g 2.1.  |t Introduction --   |g 2.2.  |t Definition --   |g 2.3.  |t Mathematical Formalism --   |g 2.4.  |t Current Technologies --   |g 2.4.1.  |t Digital Image Analysis Methodologies --   |g 2.4.1.1.  |t Image Segmentation --   |g 2.4.1.2.  |t Feature Extraction/Selection --   |g 2.4.1.3.  |t Classification --   |g 2.5.  |t Overview of Different Thresholding Techniques --   |g 2.5.1.  |t Ramesh's Algorithm --   |g 2.5.2.  |t Shanbag's Algorithm --   |g 2.5.3.  |t Correlation Coefficient --   |g 2.5.4.  |t Pun's Algorithm --   |g 2.5.5.  |t Wu's Algorithm --   |g 2.5.6.  |t Renyi's Algorithm --   |g 2.5.7.  |t Yen's Algorithm --   |g 2.5.8.  |t Johannsen's Algorithm --   |g 2.5.9.  |t Silva's Algorithm --   |g 2.5.10.  |t Fuzzy Algorithm --   |g 2.5.11.  |t Brink's Algorithm --   |g 2.5.12.  |t Otsu's Algorithm --   |g 2.5.13.  |t Kittler's Algorithm --   |g 2.5.14.  |t Li's Algorithm --   |g 2.5.15.  |t Kapur's Algorithm --   |g 2.5.16.  |t Huang's Algorithm --   |g 2.6.  |t Applications of Image Analysis --   |g 2.7.  |t Conclusion --   |g 2.8.  |t Summary --   |t Exercise Questions --   |g 3.1.  |t Introduction --   |g 3.1.1.  |t Impact on Controlling Parameters --   |g 3.2.  |t Genetic Algorithms --   |g 3.2.1.  |t Fundamental Principles and Features --   |g 3.2.2.  |t Pseudo-code of Genetic Algorithms --   |g 3.2.3.  |t Encoding Strategy and the Creation of Population --   |g 3.2.4.  |t Evaluation Techniques --   |g 3.2.5.  |t Genetic Operators --   |g 3.2.6.  |t Selection Mechanism --   |g 3.2.7.  |t Crossover --   |g 3.2.8.  |t Mutation --   |g 3.3.  |t Particle Swarm Optimization --   |g 3.3.1.  |t Pseudo-code of Particle Swarm Optimization --   |g 3.3.2.  |t PSO: Velocity and Position Update --   |g 3.4.  |t Ant Colony Optimization --   |g 3.4.1.  |t Stigmergy in Ants: Biological Inspiration --   |g 3.4.2.  |t Pseudo-code of Ant Colony Optimization --   |g 3.4.3.  |t Pheromone Trails --   |g 3.4.4.  |t Updating Pheromone Trails --   |g 3.5.  |t Differential Evolution --   |g 3.5.1.  |t Pseudo-code of Differential Evolution --   |g 3.5.2.  |t Basic Principles of DE --   |g 3.5.3.  |t Mutation --   |g 3.5.4.  |t Crossover --   |g 3.5.5.  |t Selection --   |g 3.6.  |t Simulated Annealing --   |g 3.6.1.  |t Pseudo-code of Simulated Annealing --   |g 3.6.2.  |t Basics of Simulated Annealing --   |g 3.7.  |t Tabu Search --   |g 3.7.1.  |t Pseudo-code of Tabu Search --   |g 3.7.2.  |t Memory Management in Tabu Search --   |g 3.7.3.  |t Parameters Used in Tabu Search --   |g 3.8.  |t Conclusion --   |g 3.9.  |t Summary --   |t Exercise Questions --   |g 4.1.  |t Introduction --   |g 4.2.  |t Quantum Inspired Genetic Algorithm --   |g 4.2.1.  |t Initialize the Population of Qubit Encoded Chromosomes --   |g 4.2.2.  |t Perform Quantum Interference --   |g 4.2.2.1.  |t Generate Random Chaotic Map for Each Qubit State --   |g 4.2.2.2.  |t Initiate Probabilistic Switching Between Chaotic Maps --   |g 4.2.3.  |t Find the Threshold Value in Population and Evaluate Fitness --   |g 4.2.4.  |t Apply Selection Mechanism to Generate a New Population --   |g 4.2.5.  |t Foundation of Quantum Crossover --   |g 4.2.6.  |t Foundation of Quantum Mutation --   |g 4.2.7.  |t Foundation of Quantum Shift --   |g 4.2.8.  |t Complexity Analysis --   |g 4.3.  |t Quantum Inspired Particle Swarm Optimization --   |g 4.3.1.  |t Complexity Analysis --   |g 4.4.  |t Implementation Results --   |g 4.4.1.  |t Experimental Results (Phase I) --   |g 4.4.1.1.  |t Implementation Results for QEA --   |g 4.4.2.  |t Experimental Results (Phase II) --   |g 4.4.2.1.  |t Experimental Results of Proposed QIGA and Conventional GA --   |g 4.4.2.2.  |t Results Obtained with QEA --   |g 4.4.3.  |t Experimental Results (Phase III) --   |g 4.4.3.1.  |t Results Obtained with Proposed QIGA and Conventional GA --   |g 4.4.3.2.  |t Results obtained from QEA --   |g 4.5.  |t Comparative Analysis among the Participating Algorithms --   |g 4.6.  |t Conclusion --   |g 4.7.  |t Summary --   |t Exercise Questions --   |t Coding Examples --   |g 5.1.  |t Introduction --   |g 5.2.  |t Quantum Inspired Genetic Algorithm --   |g 5.2.1.  |t Population Generation --   |g 5.2.2.  |t Quantum Orthogonality --   |g 5.2.3.  |t Determination of Threshold Values in Population and Measurement of Fitness --   |g 5.2.4.  |t Selection --   |g 5.2.5.  |t Quantum Crossover --   |g 5.2.6.  |t Quantum Mutation --   |g 5.2.7.  |t Complexity Analysis --   |g 5.3.  |t Quantum Inspired Particle Swarm Optimization --   |g 5.3.1.  |t Complexity Analysis --   |g 5.4.  |t Quantum Inspired Differential Evolution --   |g 5.4.1.  |t Complexity Analysis --   |g 5.5.  |t Quantum Inspired Ant Colony Optimization --   |g 5.5.1.  |t Complexity Analysis --   |g 5.6.  |t Quantum Inspired Simulated Annealing --   |g 5.6.1.  |t Complexity Analysis --   |g 5.7.  |t Quantum Inspired Tabu Search --   |g 5.7.1.  |t Complexity Analysis --   |g 5.8.  |t Implementation Results --   |g 5.8.1.  |t Consensus Results of the Quantum Algorithms --   |g 5.9.  |t Comparison of QIPSO with Other Existing Algorithms --   |g 5.10.  |t Conclusion --   |g 5.11.  |t Summary --   |t Exercise Questions --   |t Coding Examples --   |g 6.1.  |t Introduction --   |g 6.2.  |t Background --   |g 6.3.  |t Quantum Inspired Ant Colony Optimization --   |g 6.3.1.  |t Complexity Analysis --   |g 6.4.  |t Quantum Inspired Differential Evolution --   |g 6.4.1.  |t Complexity Analysis --   |g 6.5.  |t Quantum Inspired Particle Swarm Optimization --   |g 6.5.1.  |t Complexity Analysis --   |g 6.6.  |t Quantum Inspired Genetic Algorithm --   |g 6.6.1.  |t Complexity Analysis --   |g 6.7.  |t Quantum Inspired Simulated Annealing --   |g 6.7.1.  |t Complexity Analysis --   |g 6.8.  |t Quantum Inspired Tabu Search --   |g 6.8.1.  |t Complexity Analysis --   |g 6.9.  |t Implementation Results --   |g 6.9.1.  |t Experimental Results (Phase I) --   |g 6.9.1.1.  |t Stability of the Comparable Algorithms --   |g 6.9.2.  |t Performance Evaluation of the Comparable Algorithms of Phase I --   |g 6.9.3.  |t Experimental Results (Phase II) --   |g 6.9.4.  |t Performance Evaluation of the Participating Algorithms of Phase II --   |g 6.10.  |t Conclusion --   |g 6.11.  |t Summary --   |t Exercise Questions --   |t Coding Examples --   |g 7.1.  |t Introduction --   |g 7.2.  |t Multi-objective Optimization --   |g 7.3.  |t Experimental Methodology for Gray-Scale Multi-Level Image Thresholding --   |g 7.3.1.  |t Quantum Inspired Non-dominated Sorting-Based Multi-objective Genetic Algorithm --   |g 7.3.2.  |t Complexity Analysis --   |g 7.3.3.  |t Quantum Inspired Simulated Annealing for Multi-objective Algorithms --   |g 7.3.3.1.  |t Complexity Analysis --   |g 7.3.4.  |t Quantum Inspired Multi-objective Particle Swarm Optimization --   |g 7.3.4.1.  |t Complexity Analysis --   |g 7.3.5.  |t Quantum Inspired Multi-objective Ant Colony Optimization --   |g 7.3.5.1.  |t Complexity Analysis --   |g 7.4.  |t Implementation Results --   |g 7.4.1.  |t Experimental Results --   |g 7.4.1.1.  |t Results of Multi-Level Thresholding for QINSGA-II, NSGA-II, and SMS-EMOA --   |g 7.4.1.2.  |t Stability of the Comparable Methods --   |g 7.4.1.3.  |t Performance Evaluation --   |g 7.5.  |t Conclusion --   |g 7.6.  |t Summary --   |t Exercise Questions --   |t Coding Examples. 
533 |a Electronic reproduction.  |b Ann Arbor, MI  |n Available via World Wide Web. 
588 |a Description based on online resource; title from digital title page (viewed on August 14, 2019). 
650 0 |a Image segmentation. 
650 0 |a Image analysis. 
650 0 |a Metaheuristics. 
650 0 |a Heuristic algorithms. 
700 1 |a Bhattacharyya, Siddhartha,  |d 1975-  |e author. 
700 1 |a Maulik, Ujjwal,  |e author. 
710 2 |a ProQuest (Firm) 
776 0 8 |i Print version:  |a Dey, Sandip, 1977- author.  |t Quantum inspired meta-heuristics for image analysis  |d Hoboken, NJ : Wiley, 2019  |z 9781119488750  |w (DLC) 2019001402 
856 4 0 |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=5781623  |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)  |t 0 
907 |a .b33334262  |b 240625  |c 190819 
998 |a uww  |b    |c m  |d z   |e l  |f eng  |g nju  |h 0 
917 |a YBP DDA 
919 |a .ulebk  |b 2017-02-14 
999 f f |i daaddf3c-bebe-5882-97a9-24339c37bd12  |s 3398a71a-54dd-5fd4-9fa9-f16724203d44  |t 0 

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