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Hybrid Soft Computing for Multilevel Image and Data Segmentation 1st ed. 2016 [Kõva köide]

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  • Formaat: Hardback, 235 pages, kõrgus x laius: 235x155 mm, kaal: 5029 g, 39 Illustrations, color; 60 Illustrations, black and white; XIV, 235 p. 99 illus., 39 illus. in color., 1 Hardback
  • Sari: Computational Intelligence Methods and Applications
  • Ilmumisaeg: 18-Nov-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319475231
  • ISBN-13: 9783319475233
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Hybrid Soft Computing for Multilevel Image and Data Segmentation 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 235 pages, kõrgus x laius: 235x155 mm, kaal: 5029 g, 39 Illustrations, color; 60 Illustrations, black and white; XIV, 235 p. 99 illus., 39 illus. in color., 1 Hardback
  • Sari: Computational Intelligence Methods and Applications
  • Ilmumisaeg: 18-Nov-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319475231
  • ISBN-13: 9783319475233
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319475233.html
Märksõnad:
This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

Introduction.- Image Segmentation: A Review.- Self-supervised Gray Level Image Segmentation Using an Optimized MUSIG (OptiMUSIG) Activation Function.- Self-supervised Color Image Segmentation Using Parallel OptiMUSIG (ParaOptiMUSIG) Activation Function.- Self-supervised Gray Level Image Segmentation Using Multiobjective Based Optimized MUSIG (OptiMUSIG) Activation Function.- Self-supervised Color Image Segmentation Using Multiobjective Based Parallel Optimized MUSIG (ParaOptiMUSIG) Activation Function.- Unsupervised Genetic Algorithm Based Automatic Image Segmentation and Data Clustering Technique Validated by Fuzzy Intercluster Hostility Index.
1 Introduction
1(28)
1.1 Introduction
1(4)
1.2 Different Approaches Used for Image Segmentation
5(2)
1.2.1 Classical Approaches
5(1)
1.2.2 Soft Computing Approaches
6(1)
1.2.3 Hybrid Approaches
6(1)
1.3 Soft Computing Techniques
7(12)
1.3.1 Neural Network
7(3)
1.3.2 Fuzzy Sets and Fuzzy Logic
10(2)
1.3.3 Fuzzy Set Theory
12(2)
1.3.4 Genetic Algorithms
14(4)
1.3.5 Classical Differential Evolution
18(1)
1.4 Segmentation
19(2)
1.5 Role of Optimisation
21(6)
1.5.1 Single-Objective Optimisation
22(1)
1.5.2 Multi-objective Optimisation
22(5)
1.6 Organisation of the Book
27(2)
2 Image Segmentation: A Review
29(12)
2.1 Introduction
29(1)
2.2 Classical Approaches to Image Segmentation
29(4)
2.3 Soft Computing Approaches to Image Segmentation
33(8)
2.3.1 Neural Network Based Image Segmentation
34(2)
2.3.2 Fuzzy Based Image Segmentation
36(2)
2.3.3 Genetic Algorithm Based Image Segmentation
38(3)
3 Self-supervised Grey Level Image Segmentation Using an Optimised MUSIG (OptiMUSIG) Activation Function
41(48)
3.1 Introduction
41(2)
3.2 Mathematical Prerequisites
43(3)
3.2.1 Fuzzy c-Means
43(1)
3.2.2 Complexity Analysis of Genetic Algorithm
44(2)
3.3 Multilayer Self-organising Neural Network (MLSONN) Architecture
46(3)
3.3.1 Operating Principle
46(1)
3.3.2 Network Error Adjustment
47(1)
3.3.3 Self-Organisation Algorithm
48(1)
3.4 Optimised Multilevel Sigmoidal (OptiMUSIG) Activation Function
49(2)
3.5 Evaluation of Segmentation Efficiency
51(2)
3.5.1 Correlation Coefficient (p)
51(1)
3.5.2 Empirical Goodness Measures
52(1)
3.6 Methodology
53(2)
3.6.1 Generation of Optimised Class Boundaries
53(1)
3.6.2 Designing of OptiMUSIG Activation Function
54(1)
3.6.3 Multilevel Image Segmentation by OptiMUSIG
54(1)
3.7 Results
55(30)
3.7.1 Quantitative Performance Analysis of Segmentation
57(16)
3.7.2 Multilevel Image Segmentation Outputs
73(12)
3.8 Discussions and Conclusion
85(4)
4 Self-supervised Colour Image Segmentation Using Parallel OptiMUSIG (ParaOptiMUSIG) Activation Function
89(36)
4.1 Introduction
89(3)
4.2 Parallel Self-Organising Neural Network (PSONN) Architecture
92(1)
4.3 Parallel optimised Multilevel Sigmoidal (ParaOptiMUSIG) Activation Function
93(2)
4.4 ParaOptiMUSIG Activation Function Based Colour Image Segmentation Scheme
95(5)
4.4.1 Optimised Class Boundaries Generation for True Colour Images
96(2)
4.4.2 ParaOptiMUSIG Activation Function Design
98(1)
4.4.3 Input of True Colour Image Pixel Values to the Source Layer of the PSONN Architecture
98(1)
4.4.4 Distribution of the Colour Component Images to Three Individual SONNs
99(1)
4.4.5 Segmentation of Colour Component Images by Individual SONNs
99(1)
4.4.6 Fusion of Individual Segmented Component Outputs into a True Colour Image at the Sink Layer of the PSONN Architecture
99(1)
4.5 Experimental Results
100(19)
4.5.1 Quantitative Performance Analysis of Segmentation
100(3)
4.5.2 True Colour Image Segmentation Outputs
103(16)
4.6 Discussions and Conclusion
119(6)
5 Self-supervised Grey Level Image Segmentation Using Multi-Objective-Based Optimised MUSIG (OptiMUSIG) Activation Function
125(28)
5.1 Introduction
125(2)
5.2 Multilevel Grey scale Image Segmentation by Multi-objective Genetic Algorithm-Based OptiMUSIG Activation Function
127(12)
5.2.1 Methodology
128(2)
5.2.2 Experimental Results
130(6)
5.2.3 Image Segmentation Outputs
136(3)
5.3 NSGA-II-Based OptiMUSIG Activation Function
139(13)
5.3.1 Multilevel Greyscale Image Segmentation by NSGA-II-Based OptiMUSIG Activation Function
141(2)
5.3.2 Result Analysis
143(6)
5.3.3 Image Segmentation Outputs
149(3)
5.4 Discussions and Conclusion
152(1)
6 Self-supervised Colour Image Segmentation Using Multiobjective Based Parallel Optimized MUSIG (ParaOptiMUSIG) Activation Function
153(40)
6.1 Introduction
153(2)
6.2 Colour Image Segmentation by a Multiobjective Genetic Algorithm Based ParaOptiMUSIG Activation Function
155(16)
6.2.1 Methodology
155(3)
6.2.2 Experimental Results
158(11)
6.2.3 Image Segmentation Outputs
169(2)
6.3 NSGA II Based Parallel Optimized Multilevel Sigmoidal (ParaOptiMUSIG) Activation Function
171(19)
6.3.1 Colour Image Segmentation By NSGA II Based ParaOptiMUSIG Activation Function
173(4)
6.3.2 Result Analysis
177(11)
6.3.3 Image Segmentation Outputs
188(2)
6.4 Discussions and Conclusion
190(3)
7 Unsupervised Genetic Algorithm Based Automatic Image Segmentation and Data Clustering Technique Validated by Fuzzy Intercluster Hostility Index
193(26)
7.1 Introduction
193(3)
7.2 Region Based Image Clustering
196(2)
7.2.1 Fuzzy Intercluster Hostility Index
197(1)
7.3 Cluster Validity Indices
198(2)
7.3.1 Davies-Bouldin (DB) Validity Index
198(1)
7.3.2 CS Measure
199(1)
7.4 Automatic Clustering Differential Evolution (ACDE) Algorithm
200(1)
7.5 GA-Based Clustering Algorithm Validated by Fuzzy Intercluster Hostility Index
201(2)
7.6 Results
203(14)
7.7 Discussions and Conclusion
217(2)
References 219(14)
Index 233