Optimisation in Signal and Image Processing

by Patrick Siarry

Engineers daily encounter technological problems of growing complexity which occur in very diverse fields, such as telecommunication, genomics ad biomedical engineering. The problem to be solved can often be expressed in the form of an optimization problem: an objective function is defined, one that seeks to minimize vis-a-vis all parameters concerned. This book describes some of 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; metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization, estimation of distribution algorithms and artificial immune systems).

352 pages, Hardcover

First published September 10, 2007