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E-raamat: Bio-Inspired Computation and Applications in Image Processing

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(School of Science and Technology, Middlesex University, UK), (Assistant Professor, Sao Paulo State University (UNESP), Brazil; Visiting scholar, Harvard University, Cambridge, MA, USA)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 09-Aug-2016
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128045374
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Bio-Inspired Computation and Applications in Image ProcessingSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 09-Aug-2016
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128045374
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This book summarizes the latest developments of bio-inspired computation in image processing, with a focus on nature-inspired algorithms linked with deep learning, such as ant colony optimization, particle swarm optimization, cuckoo search, bat algorithm and firefly algorithms, which have recently emerged in the field.

In addition to documenting state-of-the-art developments, this book also discusses future research trends in bio-inspired computation, helping researchers establish possible new research avenues to pursue.

  • Reviews the latest developments in bio-inspired computation in image processing
  • Focuses on the introduction and analysis of the key bio-inspired methods and techniques
  • Combines theory with real-world applications in image processing
  • Helps solve complex problems in image and signal processing
  • Contains a diverse range of self-contained case studies in real-world applications

Muu info

Presents the latest developments in bio-inspired computation in image processing, with a focus on nature-inspired algorithms linked to deep learning
List of Contributors xiii
About the Editors xvii
Preface xix
Chapter 1 Bio-Inspired Computation and Its Applications in Image Processing: An Overview 1(24)
X.S. Yang
J.P. Papa
1 Introduction
2(1)
2 Image Processing and Optimization
3(3)
2.1 Image Segmentation Via Optimization
3(1)
2.2 Optimization
4(2)
3 Some Key Issues in Optimization
6(3)
3.1 Efficiency of an Algorithm
6(1)
3.2 How to Choose Algorithms?
7(1)
3.3 Time and Resource Constraints
8(1)
4 Nature-Inspired Optimization Algorithms
9(7)
4.1 Bio-Inspired Algorithms Based on Swarm Intelligence
9(4)
4.2 Nature-Inspired Algorithms not Based on Swarm Intelligence
13(3)
4.3 Other Algorithms
16(1)
5 Artificial Neural Networks and Support Vector Machines
16(3)
5.1 Artificial Neural Networks
16(2)
5.2 Support Vector Machines
18(1)
6 Recent Trends and Applications
19(2)
7 Conclusions
21(1)
References
21(4)
Chapter 2 Fine-Tuning Enhanced Probabilistic Neural Networks Using Metaheuristic-Driven Optimization 25(22)
S.E.N. Fernandes
K.K.F. Setoue
H. Adeli
J.P. Papa
1 Introduction
25(3)
2 Probabilistic Neural Network
28(3)
2.1 Theoretical Foundation
28(2)
2.2 Enhanced Probabilistic Neural Network With Local Decision Circles
30(1)
3 Methodology and Experimental Results
31(10)
3.1 Datasets
31(1)
3.2 Experimental Setup
31(10)
4 Conclusions
41(1)
References
42(5)
Chapter 3 Fine-Tuning Deep Belief Networks Using Cuckoo Search 47(14)
D. Rodrigues
X.S. Yang
J.P. Papa
1 Introduction
47(2)
2 Theoretical Background
49(5)
2.1 Deep Belief Networks
49(3)
2.2 Deep Belief Nets
52(1)
2.3 Cuckoo Search
53(1)
3 Methodology
54(1)
3.1 Datasets
54(1)
3.2 Harmony Search and Particle Swarm Optimization
55(1)
4 Experiments and Results
55(3)
4.1 Experimental Setup
55(1)
4.2 Experimental Results
56(2)
5 Conclusions
58(1)
References
58(3)
Chapter 4 Improved Weighted Thresholded Histogram Equalization Algorithm for Digital Image Contrast Enhancement Using the Bat Algorithm 61(26)
M. Tuba
M. Jordanski
A. Arsic
1 Introduction
61(2)
2 Literature Review
63(4)
3 Bat Algorithm
67(2)
4 Our Proposed Method
69(4)
4.1 Global Histogram Equalization
69(1)
4.2 Development of Weighting Constraints With Respect to the Threshold
70(1)
4.3 Optimizing the Weighting Constraints Using the Bat Algorithm
71(2)
5 Experimental Results
73(10)
6 Conclusions
83(1)
References
84(3)
Chapter 5 Ground-Glass Opacity Nodules Detection and Segmentation Using the Snake Model 87(18)
C.W. Bong
C.C. Liew
H.Y. Lam
1 Introduction
87(2)
2 Related Works on Delineation of GGO Lesions
89(3)
3 Snake Model
92(3)
3.1 Background
92(1)
3.2 Basic Formulation
93(1)
3.3 Variants of Snake Models
94(1)
4 Proposed Framework
95(2)
4.1 Overall Framework
95(2)
4.2 Experimental Data
97(1)
5 Result and Discussion
97(3)
6 Conclusions
100(2)
References
102(3)
Chapter 6 Mobile Object Tracking Using the Modified Cuckoo Search 105(26)
T. Ljouad
A. Amine
M. Rziza
1 Introduction
106(1)
2 Metaheuristics in Image Processing: Overview
106(4)
2.1 Genetic Algorithm
107(1)
2.2 Particle Swarm Optimization
107(1)
2.3 Artificial Bee Colony Algorithm
108(1)
2.4 Ant Colony Optimization
108(1)
2.5 Particle Filter
109(1)
2.6 Firefly Algorithm
109(1)
2.7 Cuckoo Search
109(1)
3 Cuckoo Search for Object Tracking
110(11)
3.1 Single Mobile Object Tracking Using the Modified Cuckoo Search Algorithm
111(1)
3.2 Proposed Approach: Hybrid Kalman Cuckoo Search Tracker
112(5)
3.3 Experimental Results
117(4)
4 Cuckoo Search-Based Reidentification
121(6)
4.1 Proposed Parametric Representation
122(1)
4.2 MCS-Driven Reidentification Strategy
123(3)
4.3 Experimental Results
126(1)
5 Conclusions
127(1)
References
128(3)
Chapter 7 Toward Optimal Watermarking of Grayscale Images Using the Multiple Scaling Factor-Based Cuckoo Search Technique 131(26)
A. Mishra
C. Agarwal
1 Introduction
132(7)
1.1 Earlier Research Work
132(4)
1.2 Motivation and Research Contribution
136(3)
2 Cuckoo Search Algorithm
139(1)
3 Watermarking Scheme Using the Single Scaling Factor
140(2)
3.1 DWT-SVD-Based Watermark Embedding Algorithm
141(1)
3.2 Watermark Extraction Algorithm
142(1)
4 Minimizing Trade-Off Between Visual Quality and Robustness Using Single Scaling Factor
142(3)
4.1 Effect of Single Scaling Factor Over NC(W, W') Values for Signed and Attacked Lena Images
143(1)
4.2 Effect of Single Scaling Factor Over PSNR for Signed and Attacked Lena Images
144(1)
5 Cuckoo Search-Based Watermarking Algorithm to Optimize Scaling Factors
145(1)
6 Experimental Results and Discussion
146(6)
7 Conclusions and Possible Extensions of the Present Work
152(1)
References
153(4)
Chapter 8 Bat Algorithm-Based Automatic Clustering Method and Its Application in Image Processing 157(30)
S. Nandy
P.P. Sarkar
1 Introduction
157(4)
2 Bat Optimization Algorithm
161(1)
2.1 Bat Algorithm
161(1)
3 Proposed Method: Bat Algorithm-Based Clustering
162(7)
3.1 Rule-Based Statistical Hypothesis for Clustering
167(2)
4 Evaluation
169(8)
5 Image Segmentation
177(5)
5.1 Experimental Details
178(2)
5.2 Analysis Image Segmentation Result
180(2)
6 Conclusions
182(1)
References
183(4)
Chapter 9 Multitemporal Remote Sensing Image Classification by Nature-Inspired Techniques 187(34)
J. Senthilnath
X.S. Yang
1 Introduction
188(3)
2 Problem Formulation
191(4)
2.1 Illustrative Example
193(2)
3 Methodology
195(6)
3.1 Genetic Algorithm
196(2)
3.2 Particle Swarm Optimization
198(1)
3.3 Firefly Algorithm
199(2)
4 Performance Evaluation
201(1)
4.1 Root Mean Square Error
201(1)
4.2 Receiver Operating Characteristics
201(1)
5 Results and Discussion
202(14)
5.1 Study Area and Data Description
202(1)
5.2 Spectral-Spatial MODIS Data Analysis Using Unsupervised Methods
202(13)
5.3 Time Complexity Analysis
215(1)
5.4 Comparison of Unsupervised Techniques
215(1)
6 Conclusions
216(1)
References
217(4)
Chapter 10 Firefly Algorithm for Optimized Nonrigid Demons Registration 221(18)
S. Chakraborty
N. Dey
S. Samanta
A.S. Ashour
V.E. Balas
1 Introduction
221(2)
2 Related Works
223(2)
3 Material and Methods
225(3)
3.1 Binning
225(1)
3.2 Demons Registration
225(1)
3.3 Firefly Algorithm
226(2)
4 Proposed Method
228(3)
5 Results
231(4)
6 Conclusions
235(1)
References
236(3)
Chapter 11 Minimizing the Mode-Change Latency in Real-Time Image Processing Applications 239(30)
P.S. Martins
F.R. Massaro
E.L. Ursini
M.G. Carvalho
J. Real
1 Introduction
239(4)
2 Review of Earlier Work
243(4)
2.1 Offset Minimization Algorithm
243(1)
2.2 Genetic Algorithms
243(1)
2.3 Mode-Change Model
244(1)
2.4 Schedulability Analysis
245(1)
2.5 Definition of Mode-Change Latency
246(1)
3 Model and Approach to Minimization
247(3)
4 Case Studies
250(11)
4.1 Case 1: Minimizing Offsets
250(3)
4.2 Case 2: Minimizing Latency
253(3)
4.3 Case 3: Minimizing Latency and Offsets-Weights-Based Multiobjective
256(2)
4.4 Case 4: Minimizing Latency and Offsets-Multiobjective
258(2)
4.5 Case 5: Minimizing Latency and Offsets-Multiobjective With a Random Task Set
260(1)
5 Discussion
261(4)
6 Conclusions
265(2)
References
267(2)
Chapter 12 Learning OWA Filters Parameters for SAR Imagery With Multiple Polarizations 269(16)
L. Torres
J.C. Becceneri
C.C. Freitas
S.J.S. Sant'Anna
S. Sandri
1 Introduction
269(2)
2 Basic Concepts of SAR Images
271(2)
2.1 Filters for SAR Imagery
271(2)
2.2 Image Quality Assessment for SAR Images
273(1)
3 Genetic Algorithms
273(2)
4 OWA Filters
275(1)
5 Learning OWA Filters for Multiple Polarization With GAs
276(1)
6 Experiments
277(5)
7 Conclusions and Future Work
282(1)
References
283(2)
Chapter 13 Oil Reservoir Quality Assisted by Machine Learning and Evolutionary Computation 285(26)
M.C. Kuroda
A.C. Vidal
J.P. Papa
1 Introduction
285(1)
2 Field Description
286(1)
3 Database
287(1)
4 Methods
288(7)
4.1 Self-Organizing Map
290(1)
4.2 Genetic Algorithm
291(1)
4.3 Multilayer Perceptron Neural Network
292(2)
4.4 Probabilistic and Generalized Regression Neural Networks
294(1)
5 Results and Discussion
295(13)
5.1 Prediction of Electrofacies at the Well Scale
295(4)
5.2 Prediction of Electrofacies Into 3D Grid
299(3)
5.3 Prediction of Porosity Into the 3D Grid
302(5)
5.4 Geological Analysis
307(1)
6 Conclusions
308(1)
References
309(2)
Chapter 14 Solving Imbalanced Dataset Problems for High-Dimensional Image Processing by Swarm Optimization 311(12)
J. Li
S. Fong
1 Introduction
311(1)
2 Dataset and Experiment
312(4)
3 Analysis and Conclusions
316(4)
References
320(3)
Chapter 15 Retinal image Vasculature Analysis Software (RIVAS) 323(24)
B. Aliahmad
D.K. Kumar
1 Introducing RIVAS
324(1)
2 Key Features of RIVAS
325(12)
2.1 Preprocessing and Image Enhancement
325(1)
2.2 Image Segmentation (Extraction of Vascular Network, Skeletonization, Vessel to Background Ratio)
325(2)
2.3 Automatic Measure of Optic Nerve Head Parameters (Center, Rim, Best Fitting Circle, and Color)
327(1)
2.4 Vessel Diameter Measurement (Individual, LDR, Vessel Summary-CRAE, CRVE)
327(8)
2.5 Fractal Dimension [ Binary and Differential (3D) Box-Count, Fourier, and Higuchi's]
335(1)
2.6 Analysis of the Branching Angle (Total Number, Average, Max, Min, SD, Acute Angle, Vessel Tortuosity)
336(1)
2.7 Detection of the Area of Neovascularization and Avascularized Region in a Mouse Model
337(1)
3 Application Examples
337(5)
3.1 Relationship Between Diabetes and Grayscale Fractal Dimensions of Retinal Vasculature
338(1)
3.2 10-Year Stroke Prediction
339(1)
3.3 Visualization of Fine Retinal Vessel Pulsation
340(1)
3.4 Automated Measurement of Vascular Parameters in Mouse Retinal Flat-Mounts
341(1)
References
342(5)
Index 347
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022). Joao Paulo Papa obtained his Ph.D. in Computer Science from University of Campinas, Brazil, in 2008, and was a visiting scholar at Harvard University from 2014-2015. He has been a Professor at Sao Paulo State University (UNESP), Brazil, since 2009, and his main interests include image processing, machine learning and meta-heuristic optimization.
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