Book description
This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization, estimation of distribution algorithms, and artificial immune systems).
Table of contents
- Coverpage
- Titlepage
- Copyright
- Table of Contents
- Introduction
- Chapter 1. Modeling and Optimization in Image Analysis
-
Chapter 2. Artificial Evolution and the Parisian Approach. Applications in the Processing of Signals and Images
- 2.1. Introduction
- 2.2. The Parisian approach for evolutionary algorithms
- 2.3. Applying the Parisian approach to inverse IFS problems
- 2.4. Results obtained on the inverse problems of IFS
- 2.5. Conclusion on the usage of the Parisian approach for inverse IFS problems
- 2.6. Collective representation: the Parisian approach and the Fly algorithm
- 2.7. Conclusion
- 2.8. Acknowledgements
- 2.9. Bibliography
- Chapter 3. Wavelets and Fractals for Signal and Image Analysis
- Chapter 4. Information Criteria: Examples of Applications in Signal and Image Processing
- Chapter 5. Quadratic Programming and Machine Learning – Large Scale Problems and Sparsity
- Chapter 6. Probabilistic Modeling of Policies and Application to Optimal Sensor Management
- Chapter 7. Optimizing Emissions for Tracking and Pursuit of Mobile Targets
-
Chapter 8. Bayesian Inference and Markov Models
- 8.1. Introduction and application framework
- 8.2. Detection, segmentation and classification
- 8.3. General modeling
- 8.4. Segmentation using the causal-in-scale Markov model
- 8.5. Segmentation into three classes
- 8.6. The classification of objects
- 8.7. The classification of seabeds
- 8.8. Conclusion and perspectives
- 8.9. Bibliography
- Chapter 9. The Use of Hidden Markov Models for Image Recognition: Learning with Artificial Ants, Genetic Algorithms and Particle Swarm Optimization
- Chapter 10. Biological Metaheuristics for Road Sign Detection
- Chapter 11. Metaheuristics for Continuous Variables. The Registration of Retinal Angiogram Images
- Chapter 12. Joint Estimation of the Dynamics and Shape of Physiological Signals through Genetic Algorithms
-
Chapter 13. Using Interactive Evolutionary Algorithms to Help Fit Cochlear Implants
- 13.1. Introduction
- 13.2. Choosing an optimization algorithm
- 13.3. Adapting an evolutionary algorithm to the interactive fitting of cochlear implants
- 13.4. Evaluation
- 13.5. Experiments
- 13.6. Medical issues which were raised during the experiments
- 13.7. Algorithmic conclusions for patient A
- 13.8. Conclusion
- 13.9. Bibliography
- List of Authors
- Index
Product information
- Title: Optimisation in Signal and Image Processing
- Author(s):
- Release date: October 2009
- Publisher(s): Wiley
- ISBN: 9781848210448
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