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000856219 019__ $$a1047619933$$a1050757449
000856219 020__ $$a9783319776255$$q(electronic book)
000856219 020__ $$a3319776258$$q(electronic book)
000856219 020__ $$z9783319776248
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000856219 0247_ $$a10.1007/978-3-319-77625-5$$2doi
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000856219 035__ $$aSP(OCoLC)1046977817$$z(OCoLC)1047619933$$z(OCoLC)1050757449
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000856219 049__ $$aISEA
000856219 050_4 $$aTA1637
000856219 08204 $$a621.367$$223
000856219 24500 $$aHybrid metaheuristics for image analysis /$$cSiddhartha Bhattacharyya, editor.
000856219 264_1 $$aCham :$$bSpringer,$$c[2018]
000856219 264_4 $$c©2018
000856219 300__ $$a1 online resource.
000856219 336__ $$atext$$btxt$$2rdacontent
000856219 337__ $$acomputer$$bc$$2rdamedia
000856219 338__ $$aonline resource$$bcr$$2rdacarrier
000856219 347__ $$atext file$$bPDF$$2rda
000856219 504__ $$aIncludes bibliographical references and index.
000856219 5050_ $$aIntro; Preface; Contents; Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms; 1 Introduction; 1.1 Heuristic and Metaheuristic Algorithms; 1.2 Image Segmentation; 1.3 Characteristics of the Remote Sensing Images; 1.4 Satellite Image Types and Sources; 2 Evolutionary Algorithms; 2.1 Genetic Algorithm; 2.2 Hill-Climbing Algorithm; 2.3 Hybrid Genetic Algorithm; 2.4 Example of HyGA Segmentation; 3 Dynamic Genetic Algorithm; 3.1 Structure of the Dynamic Genetic Algorithm; 3.2 Example of Hybrid Dynamic GA (HyDyGA)
000856219 5058_ $$a4 New Methods of Cooperation Between Metaheuristics and Other Algorithms4.1 Hybrid Genetic Algorithm (HyGA) and Self-Organizing Maps (SOMs); 4.2 Hybrid Dynamic (GA) and Fuzzy C-Means (FCM); 4.3 Examples of Image Segmentation Using SOMs-HyGA; 4.4 Examples of Satellite Image Segmentation Using FCM-HyDyGA; 5 Metaheuristic Performance Analysis; 5.1 Metaheuristic Algorithm Complexity Analysis; 5.2 Robustness and Efficiency Analysis; 5.3 Responsiveness Analysis; 6 Discussion; 7 Conclusion; References; A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation
000856219 5058_ $$a1 Introduction2 Related Works; 3 Overview of Quantum Computing; 3.1 Definition of a Quantum Bit; 3.2 Quantum Register; 3.3 Quantum Measure; 3.4 Quantum Algorithms; 4 Quantum Genetic Algorithm Principles; 4.1 Coding of Quantum Chromosomes; 4.2 Measuring Chromosomes; 4.3 Quantum Genetic Operations; 5 The Proposed Approach; 5.1 From Cellular Automata to Chromosome; 5.2 Initialization; 5.3 Measure of Quantum Chromosomes; 5.4 Evaluation of Solutions; 5.5 Updating Chromosomes by Interference; 5.6 Updating of Best Solutions; 6 Experimental Results; 7 Comparison Between Quantum GA and Conventional GA
000856219 5058_ $$a7.1 Visual Results7.2 Numerical Results; 8 Conclusion; References; Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers; 1 Introduction; 2 Feature Extraction; 2.1 Gabor Filters; 2.2 Local Binary Patterns and Orthogonal Combination of Local Binary Patterns; 2.3 Histogram of Oriented Gradients; 3 Distance-Based Classification; 4 Proposed Hybrid Metaheuristic GA-SVM Model for Classification; 4.1 Support Vector Machines; 4.2 Genetic Algorithm; 4.3 Chromosome Design; 4.4 Fitness Function; 4.5 Design of the Proposed GA-SVM Model
000856219 5058_ $$a5 Proposed Hybrid Face Recognition Approaches5.1 Integrating OC-LBP and HOG Features; 5.2 Gabor Filtered Zernike Moments; 6 Empirical Evaluation; 6.1 Datasets Used; 6.1.1 ORL Database; 6.1.2 Yale Database; 6.1.3 FERET Database; 6.2 Implementation Parameters; 6.3 Database Generation for Validation; 6.4 Performance Evaluation of the Integrated OC-LBP and HOG Approaches; 6.5 Performance Evaluation of the Gabor Filtered ZM Method; 7 Performance Comparison with Other Similar and State-of-the-Art Methods; 8 Performance Evaluation on the Self-generated Database
000856219 506__ $$aAccess limited to authorized users.
000856219 520__ $$aThis book presents contributions in the field of computational intelligence for the purpose of image analysis. The chapters discuss how problems such as image segmentation, edge detection, face recognition, feature extraction, and image contrast enhancement can be solved using techniques such as genetic algorithms and particle swarm optimization. The contributions provide a multidimensional approach, and the book will be useful for researchers in computer science, electrical engineering, and information technology.
000856219 588__ $$aOnline resource; title from PDF title page (viewed August 7, 2018).
000856219 650_0 $$aImage analysis.
000856219 650_0 $$aComputational intelligence.
000856219 650_0 $$aMetaheuristics.
000856219 650_0 $$aHeuristic programming.
000856219 7001_ $$aBhattacharyya, Siddhartha,$$d1975-$$eeditor.
000856219 77608 $$iPrint version: $$z331977624X$$z9783319776248$$w(OCoLC)1022789851
000856219 852__ $$bebk
000856219 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-77625-5$$zOnline Access$$91397441.1
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