Optimisation in Signal and Image ProcessingPatrick Siarry 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
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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according active adapted algorithm analysis applications approach associated average calculated carried chapter complex Computer considered convergence corresponds created criteria criterion defined depends described detection determine dimension distance distribution equation estimation evaluation evolutionary example exist experiments exponent expressed field Figure fitness fractal function genetic given global IEEE increases individual initial intervals iteration known leads learning linked Markov maxima maximum means measurements metaheuristics method minimization Note objects observations obtained operator optimization parameters particular phase pixels points population position possible presented probability problem programming random referred registration represents resolution road sampling scale segmentation selection sensors sequence shape shows signal signs similar simulated singularities solution solve space techniques tests textures transform variables vector wavelet