Optimisation in Signal and Image Processing [Hardcover]

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).Introduction xiii

Chapter 1. Modeling and Optimization in Image Analysis 1
Jean Louchet

1.1. Modeling at the source of image analysis and synthesis 1

1.2. From image synthesis to analysis 2

1.3. Scene geometric modeling and image synthesis 3

1.4. Direct model inversion and the Hough transform 4

1.5. Optimization and physical modeling 9

1.6. Conclusion 12

1.7. Acknowledgements 13

1.8. Bibliography 13

Chapter 2. Artificial Evolution and the Parisian Approach. Applications in the Processing of Signals and Images 15
Pierre Collet and Jean Louchet

2.1. Introduction 15

2.2. The Parisian approach for evolutionary algorithms 15

2.3. Applying the Parisian approach to inverse IFS problems 17

2.4. Results obtained on the inverse problems of IFS 20

2.5. Conclusion on the usage of the Parisian approach for inverse IFS problems 22

2.6. Collective representation: the Parisian approach and the Fly algorithm 23

2.7. Conclusion 40

2.8. Acknowledgements 41

2.9.Bibliography 41

Chapter 3. Wavelets and Fractals for Signal and Image Anal£V