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Optimisation in Signal and Image Processing by Patrick Siarry

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Table of Contents

Introduction

Chapter 1. Modeling and Optimization in Image Analysis

Jean LOUCHET

1.1. Modeling at the source of image analysis and synthesis

1.2. From image synthesis to analysis

1.3. Scene geometric modeling and image synthesis

1.4. Direct model inversion and the Hough transform

1.4.1. The deterministic Hough transform

1.4.2. Stochastic exploration of parameters: evolutionary Hough

1.4.3. Examples of generalization

1.5. Optimization and physical modeling

1.5.1. Photometric modeling

1.5.2. Motion modeling

1.6. Conclusion

1.7. Acknowledgements

1.8. Bibliography

Chapter 2. Artificial Evolution and the Parisian Approach. Applications in the Processing of Signals and Images

Pierre COLLET and Jean LOUCHET

2.1. Introduction

2.2. The Parisian approach for evolutionary algorithms

2.3. Applying the Parisian approach to inverse IFS problems

2.3.1. Choosing individuals for the evaluation process

2.3.2. Retribution of individuals

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

2.6.1. The principles

2.6.2. Results on real images

2.6.3. Application to robotics: fly-based robot planning

2.6.4. Sensor fusion

2.6.5. Artificial evolution and real time

2.6.6. Conclusion about the fly algorithm

2.7. Conclusion

2.8. Acknowledgements

2.9. Bibliography

Chapter 3. Wavelets and Fractals for Signal and Image Analysis

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