Optimisation in Signal and Image Processing

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Patrick Siarry
John Wiley & Sons, Mar 1, 2013 - Technology & Engineering - 352 pages
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).
 

Contents

Introduction
Biological Metaheuristics for Road Sign
Information Criteria Examples
Metaheuristics for Continuous Variables
Artificial Evolution and the Parisian
uadratic Pro rammin and Machine
Optimizing Emissions for Tracking
Bayesian Inference and Markov Models
The Use of Hidden Markov Models
Using Interactive Evolutionaq Algorithms
Joint Estimation of the Dynamics
List of Authors
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About the author (2013)

Patrick Siarry is a Professor of Automatics and Informatics at the University of Paris-Est Créteil, where he leads the Image and Signal Processing team in the Laboratoire Images, Signaux et Systèmes Intelligents - LiSSi.

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