Bio-Inspired Computation and Applications in Image Processing

 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 9. August 2016
  • |
  • 374 Seiten
 
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978-0-12-804537-4 (ISBN)
 

Bio-Inspired Computation and Applications in Image Processing summarizes the latest developments in bio-inspired computation in image processing, focusing on nature-inspired algorithms that are linked with deep learning, such as ant colony optimization, particle swarm optimization, and bat and firefly algorithms that 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 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


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 at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi'an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).
  • Englisch
  • San Diego
Elsevier Science
  • 25,90 MB
978-0-12-804537-4 (9780128045374)
012804537X (012804537X)
weitere Ausgaben werden ermittelt
  • Cover
  • Title page
  • Copyright page
  • Contents
  • List of Contributors
  • About the editors
  • Preface
  • Chapter 1 - Bio-inspired computation and its applications in image processing: an overview
  • 1 - Introduction
  • 2 - Image processing and optimization
  • 2.1 - Image segmentation via optimization
  • 2.2 - Optimization
  • 3 - Some key issues in optimization
  • 3.1 - Efficiency of an algorithm
  • 3.2 - How to choose algorithms?
  • 3.3 - Time and resource constraints
  • 4 - Nature-inspired optimization algorithms
  • 4.1 - Bio-inspired algorithms based on swarm intelligence
  • 4.1.1 - Ant and bee algorithms
  • 4.1.2 - Bat algorithm
  • 4.1.3 - Particle swarm optimization
  • 4.1.4 - Firefly algorithm
  • 4.1.5 - Cuckoo search
  • 4.1.6 - Flower pollination algorithm
  • 4.2 - Nature-inspired algorithms not based on swarm intelligence
  • 4.2.1 - Simulated annealing
  • 4.2.2 - Genetic algorithms
  • 4.2.3 - Differential evolution
  • 4.2.4 - Harmony search
  • 4.3 - Other algorithms
  • 5 - Artificial neural networks and support vector machines
  • 5.1 - Artificial neural networks
  • 5.2 - Support vector machines
  • 6 - Recent trends and applications
  • 7 - Conclusions
  • References
  • Chapter 2 - Fine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization
  • 1 - Introduction
  • 2 - Probabilistic neural network
  • 2.1 - Theoretical foundation
  • 2.2 - Enhanced probabilistic neural network with local decision circles
  • 3 - Methodology and experimental results
  • 3.1 - Datasets
  • 3.2 - Experimental setup
  • 3.2.1 - PNNs versus EPNNs
  • 3.2.2 - Evaluating the EPNN and metaheuristic-based EPNNs
  • 4 - Conclusions
  • Acknowledgments
  • References
  • Chapter 3 - Fine-tuning deep belief networks using cuckoo search
  • 1 - Introduction
  • 2 - Theoretical background
  • 2.1 - Deep belief networks
  • 2.1.1 - Restricted Boltzmann machines
  • 2.1.2 - Learning algorithm
  • 2.2 - Deep belief nets
  • 2.3 - Cuckoo search
  • 3 - Methodology
  • 3.1 - Datasets
  • 3.2 - Harmony search and particle swarm optimization
  • 4 - Experiments and results
  • 4.1 - Experimental setup
  • 4.2 - Experimental results
  • 5 - Conclusions
  • Acknowledgments
  • References
  • Chapter 4 - Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using ...
  • 1 - Introduction
  • 2 - Literature review
  • 3 - Bat algorithm
  • 4 - Our proposed method
  • 4.1 - Global histogram equalization
  • 4.2 - Development of weighting constraints with respect to the threshold
  • 4.3 - Optimizing the weighting constraints using the bat algorithm
  • 5 - Experimental results
  • 6 - Conclusions
  • Acknowledgment
  • References
  • Chapter 5 - Ground-glass opacity nodules detection and segmentation using the snake model
  • 1 - Introduction
  • 2 - Related works on delineation of GGO lesions
  • 3 - Snake model
  • 3.1 - Background
  • 3.2 - Basic formulation
  • 3.3 - Variants of snake models
  • 4 - Proposed framework
  • 4.1 - Overall framework
  • 4.2 - Experimental data
  • 5 - Result and discussion
  • 6 - Conclusions
  • References
  • Chapter 6 - Mobile object tracking using the modified cuckoo search
  • 1 - Introduction
  • 2 - Metaheuristics in image processing: overview
  • 2.1 - Genetic algorithm
  • 2.2 - Particle swarm optimization
  • 2.3 - Artificial bee colony algorithm
  • 2.4 - Ant colony optimization
  • 2.5 - Particle filter
  • 2.6 - Firefly algorithm
  • 2.7 - Cuckoo search
  • 3 - Cuckoo search for object tracking
  • 3.1 - Single mobile object tracking using the modified cuckoo search algorithm
  • 3.1.1 - Problem formulation
  • 3.2 - Proposed approach: hybrid Kalman cuckoo search tracker
  • 3.3 - Experimental results
  • 4 - Cuckoo search-based reidentification
  • 4.1 - Proposed parametric representation
  • 4.2 - MCS-driven reidentification strategy
  • 4.3 - Experimental results
  • 5 - Conclusions
  • References
  • Chapter 7 - Toward optimal watermarking of grayscale images using the multiple scaling factor-based cuckoo search technique
  • 1 - Introduction
  • 1.1 - Earlier research work
  • 1.2 - Motivation and research contribution
  • 2 - Cuckoo Search Algorithm
  • 3 - Watermarking Scheme Using the Single Scaling Factor
  • 3.1 - DWT-SVD-based watermark embedding algorithm
  • 3.2 - Watermark extraction algorithm
  • 4 - Minimizing trade-Off Between Visual Quality and Robustness Using Single Scaling Factor
  • 4.1 - Effect of single scaling factor over NC(W, W9) values for signed and attacked lena images
  • 4.2 - Effect of single scaling factor over PSNR for signed and attacked lena images
  • 5 - Cuckoo Search-Based Watermarking Algorithm to Optimize Scaling Factors
  • 6 - Experimental Results and Discussion
  • 7 - Conclusions and Possible Extensions of the Present Work
  • References
  • Chapter 8 - Bat algorithm-based automatic clustering method and its application in image processing
  • 1 - Introduction
  • 2 - Bat optimization algorithm
  • 2.1 - Bat algorithm
  • 3 - Proposed method: bat algorithm-based clustering
  • 3.1 - Rule-based statistical hypothesis for clustering
  • 4 - Evaluation
  • 5 - Image segmentation
  • 5.1 - Experimental details
  • 5.2 - Analysis image segmentation result
  • 6 - Conclusions
  • References
  • Chapter 9 - Multitemporal remote sensing image classification by nature-inspired techniques
  • 1 - Introduction
  • 2 - Problem formulation
  • 2.1 - Illustrative example
  • 3 - Methodology
  • 3.1 - Genetic algorithm
  • 3.2 - Particle swarm optimization
  • 3.3 - Firefly algorithm
  • 4 - Performance evaluation
  • 4.1 - Root mean square error
  • 4.2 - Receiver operating characteristics
  • 5 - Results and discussion
  • 5.1 - Study area and data description
  • 5.2 - Spectral-spatial MODIS data analysis using unsupervised methods
  • 5.2.1 - Clustering MODIS data
  • 5.2.1.1 - Genetic algorithm
  • 5.2.1.2 - Particle swarm optimization
  • 5.2.1.3 - Firefly algorithm
  • 5.2.2 - Segmentation of clustered MODIS data
  • 5.2.3 - Qualitative assessment of GA, PSO, and FA
  • 5.3 - Time complexity analysis
  • 5.4 - Comparison of unsupervised techniques
  • 6 - Conclusions
  • Acknowledgments
  • References
  • Chapter 10 - Firefly algorithm for optimized nonrigid demons registration
  • 1 - Introduction
  • 2 - Related works
  • 3 - Material and methods
  • 3.1 - Binning
  • 3.2 - Demons registration
  • 3.3 - Firefly algorithm
  • 4 - Proposed method
  • 5 - Results
  • 6 - Conclusions
  • Acknowledgment
  • References
  • Chapter 11 - Minimizing the mode-change latency in real-time image processing applications
  • 1 - Introduction
  • 2 - Review of earlier work
  • 2.1 - Offset minimization algorithm
  • 2.2 - Genetic algorithms
  • 2.3 - Mode-change model
  • 2.4 - Schedulability analysis
  • 2.5 - Definition of mode-change latency
  • 3 - Model and approach to minimization
  • 4 - Case studies
  • 4.1 - Case 1: Minimizing offsets
  • 4.1.1 - Definition
  • 4.1.2 - GA configuration
  • 4.1.3 - Modeling
  • 4.1.4 - Results
  • 4.2 - Case 2: Minimizing latency
  • 4.2.1 - Definition
  • 4.2.2 - GA configuration
  • 4.2.3 - Modeling
  • 4.2.4 - Results
  • 4.3 - Case 3: Minimizing latency and offsets- weights-based multiobjective
  • 4.3.1 - Definition
  • 4.3.2 - GA configuration
  • 4.3.3 - Modeling
  • 4.3.4 - Results
  • 4.4 - Case 4: Minimizing latency and offsets-multiobjective
  • 4.4.1 - Definition
  • 4.4.2 - GA Configuration
  • 4.4.3 - Modeling
  • 4.4.4 - Results
  • 4.4.5 - Comparison
  • 4.5 - Case 5: Minimizing latency and offsets-multiobjective with a random task set
  • 4.5.1 - Definition
  • 4.5.2 - GA configuration and modeling
  • 5 - Discussion
  • 6 - Conclusions
  • References
  • Chapter 12 - Learning OWA filters parameters for SAR imagery with multiple polarizations
  • 1 - Introduction
  • 2 - Basic concepts of SAR images
  • 2.1 - Filters for SAR imagery
  • 2.2 - Image quality assessment for SAR images
  • 3 - Genetic algorithms
  • 4 - OWA filters
  • 5 - Learning OWA filters for multiple polarization with GAs
  • 6 - Experiments
  • 7 - Conclusions and Future Work
  • Acknowledgments
  • References
  • Chapter 13 - Oil reservoir quality assisted by machine learning and evolutionary computation
  • 1 - Introduction
  • 2 - Field Description
  • 3 - Database
  • 4 - Methods
  • 4.1 - Self-organizing map
  • 4.2 - Genetic algorithm
  • 4.3 - Multilayer perceptron neural network
  • 4.4 - Probabilistic and generalized regression neural networks
  • 5 - Results and discussion
  • 5.1 - Prediction of electrofacies at the well scale
  • 5.2 - Prediction of electrofacies into 3D grid
  • 5.3 - Prediction of porosity into the 3D grid
  • 5.3.1 - 3D porosity prediction by GRNN
  • 5.4 - Geological analysis
  • 6 - Conclusions
  • Acknowledgments
  • References
  • Chapter 14 - Solving imbalanced dataset problems for high-dimensional image processing by swarm optimization
  • 1 - Introduction
  • 2 - Dataset and experiment
  • 3 - Analysis and conclusions
  • References
  • Chapter 15 - Retinal image vasculature analysis software (RIVAS)
  • 1 - Introducing RIVAS
  • 2 - Key features of RIVAS
  • 2.1 - Preprocessing and image enhancement
  • 2.2 - Image segmentation (extraction of vascular network, skeletonization, vessel to background ratio)
  • 2.3 - Automatic measure of optic nerve head parameters (center, rim, best fitting circle, and color)
  • 2.4 - Vessel diameter measurement (individual, LDR, vessel summary-CRAE, CRVE)
  • 2.5 - Fractal dimension [binary and differential (3D) box-count, Fourier, and Higuchi's]
  • 2.6 - Analysis of the branching angle (total number, average, max, min, SD, acute angle, vessel tortuosity)
  • 2.7 - Detection of the area of neovascularization and avascularized region in a mouse model
  • 3 - Application examples
  • 3.1 - Relationship between diabetes and grayscale fractal dimensions of retinal vasculature
  • 3.2 - 10-year stroke prediction
  • 3.3 - Visualization of fine retinal vessel pulsation
  • 3.4 - Automated measurement of vascular parameters in mouse retinal flat-mounts
  • References
  • Index
  • Back cover

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