Samenvatting
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).
Specificaties
ISBN13:9781848210448
Taal:Engels
Bindwijze:gebonden
Aantal pagina's:384
Uitgever:John Wiley & Sons
Serie:ISTE
Inhoudsopgave
Introduction xiii
<p>Chapter 1. Modeling and Optimization in Image Analysis 1<br /> Jean Louchet</p>
<p>1.1. Modeling at the source of image analysis and synthesis 1</p>
<p>1.2. From image synthesis to analysis 2</p>
<p>1.3. Scene geometric modeling and image synthesis 3</p>
<p>1.4. Direct model inversion and the Hough transform 4</p>
<p>1.5. Optimization and physical modeling 9</p>
<p>1.6. Conclusion 12</p>
<p>1.7. Acknowledgements 13</p>
<p>1.8. Bibliography 13</p>
<p>Chapter 2. Artificial Evolution and the Parisian Approach. Applications in the Processing of Signals and Images 15<br /> Pierre Collet and Jean Louchet</p>
<p>2.1. Introduction 15</p>
<p>2.2. The Parisian approach for evolutionary algorithms 15</p>
<p>2.3. Applying the Parisian approach to inverse IFS problems 17</p>
<p>2.4. Results obtained on the inverse problems of IFS 20</p>
<p>2.5. Conclusion on the usage of the Parisian approach for inverse IFS problems 22</p>
<p>2.6. Collective representation: the Parisian approach and the Fly algorithm 23</p>
<p>2.7. Conclusion 40</p>
<p>2.8. Acknowledgements 41</p>
<p>2.9.Bibliography 41</p>
<p>Chapter 3. Wavelets and Fractals for Signal and Image Analysis 45<br /> Abdeldjalil Ouahabi and Djedjiga Ait Aouit</p>
<p>3.1. Introduction 45</p>
<p>3.2. Some general points on fractals 46</p>
<p>3.3. Multifractal analysis of signals 54</p>
<p>3.4. Distribution of singularities based on wavelets 60</p>
<p>3.5. Experiments 70</p>
<p>3.6. Conclusion 76</p>
<p>3.7. Bibliography 76</p>
<p>Chapter 4. Information Criteria: Examples of Applications in Signal and Image Processing 79<br /> Christian Oliver and Olivier Alata</p>
<p>4.1. Introduction and context 79</p>
<p>4.2. Overview of the different criteria 81</p>
<p>4.3. The case of auto–regressive (AR) models 83</p>
<p>4.4. Applying the process to unsupervised clustering 95</p>
<p>4.5. Law approximation with the help of histograms 98</p>
<p>4.6. Other applications 103</p>
<p>4.7. Conclusion 106</p>
<p>4.8. Appendix 106</p>
<p>4.9. Bibliography 107</p>
<p>Chapter 5. Quadratic Programming and Machine Learning Large Scale Problems and Sparsity 111<br /> Gaëlle Looslil, Stéphane Canu</p>
<p>5.1. Introduction 111</p>
<p>5.2. Learning processes and optimization 112</p>
<p>5.3. From learning methods to quadratic programming 117</p>
<p>5.4. Methods and resolution 119</p>
<p>5.5. Experiments 128</p>
<p>5.6. Conclusion 132</p>
<p>5.7. Bibliography 133</p>
<p>Chapter 6. Probabilistic Modeling of Policies and Application to Optimal Sensor Management 137<br /> Frédéric Dambreville, Francis Celeste and Cécile Simonin</p>
<p>6.1. Continuum, a path toward oblivion 137</p>
<p>6.2. The cross–entropy (CE) method 138</p>
<p>6.3. Examples of implementation of CE for surveillance 146</p>
<p>6.4. Example of implementation of CE for exploration 153</p>
<p>6.5. Optimal control under partial observation 158</p>
<p>6.6. Conclusion 166</p>
<p>6.7. Bibliography 166</p>
<p>Chapter 7. Optimizing Emissions for Tracking and Pursuit of Mobile Targets 169<br /> Jean–Pierre Le Cadre</p>
<p>7.1. Introduction 169</p>
<p>7.2. Elementary modeling of the problem (deterministic case) 170</p>
<p>7.3. Application to the optimization of emissions (deterministic case) 175</p>
<p>7.4. The case of a target with a Markov trajectory 181</p>
<p>7.5. Conclusion 189</p>
<p>7.6. Appendix: monotonous functional matrices 189</p>
<p>7.7. Bibliography 192</p>
<p>Chapter 8. Bayesian Inference and Markov Models 195<br /> Christophe Collet</p>
<p>8.1. Introduction and application framework 195</p>
<p>8.2. Detection, segmentation and classification 196</p>
<p>8.3. General modeling 199</p>
<p>8.4. Segmentation using the causal–in–scale Markov model 201</p>
<p>8.5. Segmentation into three classes 203</p>
<p>8.6. The classification of objects 206</p>
<p>8.7. The classification of seabeds 212</p>
<p>8.8. Conclusion and perspectives 214</p>
<p>8.9. Bibliography 215</p>
<p>Chapter 9. The Use of Hidden Markov Models for Image Recognition: Learning with Artificial Ants, Genetic Algorithms and Particle Swarm Optimization 219<br /> Sébastien Aupetit, Nicolas Monmarchè and Mohamed Slimane</p>
<p>9.1. Introduction 219</p>
<p>9.2. Hidden Markov models (HMMs) 220</p>
<p>9.3. Using metaheuristics to learn HMMs 223</p>
<p>9.4. Description, parameter setting and evaluation of the six approaches that are used to train HMMs 226</p>
<p>9.5. Conclusion 240</p>
<p>9.6. Bibliography 240</p>
<p>Chapter 10. Biological Metaheuristics for Road Sign Detection 245<br /> Guillaume Dutilleux and Pierre Charbonnier</p>
<p>10.1. Introduction 245</p>
<p>10.2. Relationship to existing works 246</p>
<p>10.3. Template and deformations 248</p>
<p>10.4. Estimation problem 248</p>
<p>10.5. Three biological metaheuristics 252</p>
<p>10.6. Experimental results 259</p>
<p>10.7. Conclusion 265</p>
<p>10.8. Bibliography 266</p>
<p>Chapter 11. Metaheuristics for Continuous Variables. The Registration of Retinal Angiogram Images 269<br /> Johann Drèo, Jean–Claude Nunes and Patrick Siarry</p>
<p>11.1. Introduction 269</p>
<p>11.2. Metaheuristics for difficult optimization problems 270</p>
<p>11.3. Image registration of retinal angiograms 275</p>
<p>11.4. Optimizing the image registration process 279</p>
<p>11.5. Results 288</p>
<p>11.6. Analysis of the results 295</p>
<p>11.7. Conclusion 296</p>
<p>11.8. Acknowledgements 296</p>
<p>11.9. Bibliography 296</p>
<p>Chapter 12. Joint Estimation of the Dynamics and Shape of Physiological Signals through Genetic Algorithms 301<br /> Amine Naït–Ali and Patrick Siarry</p>
<p>12.1. Introduction 301</p>
<p>12.2. Brainstem Auditory Evoked Potentials (BAEPs) 302</p>
<p>12.3. Processing BAEPs 303</p>
<p>12.4. Genetic algorithms 305</p>
<p>12.5. BAEP dynamics 307</p>
<p>12.6. The non–stationarity of the shape of the BAEPs 324</p>
<p>12.7. Conclusion 327</p>
<p>12.8. Bibliography 327</p>
<p>Chapter 13. Using Interactive Evolutionary Algorithms to Help Fit Cochlear Implants 329<br /> Pierre Collet, Pierrick Legrand, Claire Bourgeois–République, Vincent Péan and Bruno Frachet</p>
<p>13.1. Introduction 329</p>
<p>13.2. Choosing an optimization algorithm 333</p>
<p>13.3. Adapting an evolutionary algorithm to the interactive fitting of cochlear implants 335</p>
<p>13.4. Evaluation 338</p>
<p>13.5. Experiments 339</p>
<p>13.6. Medical issues which were raised during the experiments 350</p>
<p>13.7. Algorithmic conclusions for patient A 352</p>
<p>13.8. Conclusion 354</p>
<p>13.9. Bibliography 354</p>
<p>List of Authors 357</p>
<p>Index 359</p>
<p>Chapter 1. Modeling and Optimization in Image Analysis 1<br /> Jean Louchet</p>
<p>1.1. Modeling at the source of image analysis and synthesis 1</p>
<p>1.2. From image synthesis to analysis 2</p>
<p>1.3. Scene geometric modeling and image synthesis 3</p>
<p>1.4. Direct model inversion and the Hough transform 4</p>
<p>1.5. Optimization and physical modeling 9</p>
<p>1.6. Conclusion 12</p>
<p>1.7. Acknowledgements 13</p>
<p>1.8. Bibliography 13</p>
<p>Chapter 2. Artificial Evolution and the Parisian Approach. Applications in the Processing of Signals and Images 15<br /> Pierre Collet and Jean Louchet</p>
<p>2.1. Introduction 15</p>
<p>2.2. The Parisian approach for evolutionary algorithms 15</p>
<p>2.3. Applying the Parisian approach to inverse IFS problems 17</p>
<p>2.4. Results obtained on the inverse problems of IFS 20</p>
<p>2.5. Conclusion on the usage of the Parisian approach for inverse IFS problems 22</p>
<p>2.6. Collective representation: the Parisian approach and the Fly algorithm 23</p>
<p>2.7. Conclusion 40</p>
<p>2.8. Acknowledgements 41</p>
<p>2.9.Bibliography 41</p>
<p>Chapter 3. Wavelets and Fractals for Signal and Image Analysis 45<br /> Abdeldjalil Ouahabi and Djedjiga Ait Aouit</p>
<p>3.1. Introduction 45</p>
<p>3.2. Some general points on fractals 46</p>
<p>3.3. Multifractal analysis of signals 54</p>
<p>3.4. Distribution of singularities based on wavelets 60</p>
<p>3.5. Experiments 70</p>
<p>3.6. Conclusion 76</p>
<p>3.7. Bibliography 76</p>
<p>Chapter 4. Information Criteria: Examples of Applications in Signal and Image Processing 79<br /> Christian Oliver and Olivier Alata</p>
<p>4.1. Introduction and context 79</p>
<p>4.2. Overview of the different criteria 81</p>
<p>4.3. The case of auto–regressive (AR) models 83</p>
<p>4.4. Applying the process to unsupervised clustering 95</p>
<p>4.5. Law approximation with the help of histograms 98</p>
<p>4.6. Other applications 103</p>
<p>4.7. Conclusion 106</p>
<p>4.8. Appendix 106</p>
<p>4.9. Bibliography 107</p>
<p>Chapter 5. Quadratic Programming and Machine Learning Large Scale Problems and Sparsity 111<br /> Gaëlle Looslil, Stéphane Canu</p>
<p>5.1. Introduction 111</p>
<p>5.2. Learning processes and optimization 112</p>
<p>5.3. From learning methods to quadratic programming 117</p>
<p>5.4. Methods and resolution 119</p>
<p>5.5. Experiments 128</p>
<p>5.6. Conclusion 132</p>
<p>5.7. Bibliography 133</p>
<p>Chapter 6. Probabilistic Modeling of Policies and Application to Optimal Sensor Management 137<br /> Frédéric Dambreville, Francis Celeste and Cécile Simonin</p>
<p>6.1. Continuum, a path toward oblivion 137</p>
<p>6.2. The cross–entropy (CE) method 138</p>
<p>6.3. Examples of implementation of CE for surveillance 146</p>
<p>6.4. Example of implementation of CE for exploration 153</p>
<p>6.5. Optimal control under partial observation 158</p>
<p>6.6. Conclusion 166</p>
<p>6.7. Bibliography 166</p>
<p>Chapter 7. Optimizing Emissions for Tracking and Pursuit of Mobile Targets 169<br /> Jean–Pierre Le Cadre</p>
<p>7.1. Introduction 169</p>
<p>7.2. Elementary modeling of the problem (deterministic case) 170</p>
<p>7.3. Application to the optimization of emissions (deterministic case) 175</p>
<p>7.4. The case of a target with a Markov trajectory 181</p>
<p>7.5. Conclusion 189</p>
<p>7.6. Appendix: monotonous functional matrices 189</p>
<p>7.7. Bibliography 192</p>
<p>Chapter 8. Bayesian Inference and Markov Models 195<br /> Christophe Collet</p>
<p>8.1. Introduction and application framework 195</p>
<p>8.2. Detection, segmentation and classification 196</p>
<p>8.3. General modeling 199</p>
<p>8.4. Segmentation using the causal–in–scale Markov model 201</p>
<p>8.5. Segmentation into three classes 203</p>
<p>8.6. The classification of objects 206</p>
<p>8.7. The classification of seabeds 212</p>
<p>8.8. Conclusion and perspectives 214</p>
<p>8.9. Bibliography 215</p>
<p>Chapter 9. The Use of Hidden Markov Models for Image Recognition: Learning with Artificial Ants, Genetic Algorithms and Particle Swarm Optimization 219<br /> Sébastien Aupetit, Nicolas Monmarchè and Mohamed Slimane</p>
<p>9.1. Introduction 219</p>
<p>9.2. Hidden Markov models (HMMs) 220</p>
<p>9.3. Using metaheuristics to learn HMMs 223</p>
<p>9.4. Description, parameter setting and evaluation of the six approaches that are used to train HMMs 226</p>
<p>9.5. Conclusion 240</p>
<p>9.6. Bibliography 240</p>
<p>Chapter 10. Biological Metaheuristics for Road Sign Detection 245<br /> Guillaume Dutilleux and Pierre Charbonnier</p>
<p>10.1. Introduction 245</p>
<p>10.2. Relationship to existing works 246</p>
<p>10.3. Template and deformations 248</p>
<p>10.4. Estimation problem 248</p>
<p>10.5. Three biological metaheuristics 252</p>
<p>10.6. Experimental results 259</p>
<p>10.7. Conclusion 265</p>
<p>10.8. Bibliography 266</p>
<p>Chapter 11. Metaheuristics for Continuous Variables. The Registration of Retinal Angiogram Images 269<br /> Johann Drèo, Jean–Claude Nunes and Patrick Siarry</p>
<p>11.1. Introduction 269</p>
<p>11.2. Metaheuristics for difficult optimization problems 270</p>
<p>11.3. Image registration of retinal angiograms 275</p>
<p>11.4. Optimizing the image registration process 279</p>
<p>11.5. Results 288</p>
<p>11.6. Analysis of the results 295</p>
<p>11.7. Conclusion 296</p>
<p>11.8. Acknowledgements 296</p>
<p>11.9. Bibliography 296</p>
<p>Chapter 12. Joint Estimation of the Dynamics and Shape of Physiological Signals through Genetic Algorithms 301<br /> Amine Naït–Ali and Patrick Siarry</p>
<p>12.1. Introduction 301</p>
<p>12.2. Brainstem Auditory Evoked Potentials (BAEPs) 302</p>
<p>12.3. Processing BAEPs 303</p>
<p>12.4. Genetic algorithms 305</p>
<p>12.5. BAEP dynamics 307</p>
<p>12.6. The non–stationarity of the shape of the BAEPs 324</p>
<p>12.7. Conclusion 327</p>
<p>12.8. Bibliography 327</p>
<p>Chapter 13. Using Interactive Evolutionary Algorithms to Help Fit Cochlear Implants 329<br /> Pierre Collet, Pierrick Legrand, Claire Bourgeois–République, Vincent Péan and Bruno Frachet</p>
<p>13.1. Introduction 329</p>
<p>13.2. Choosing an optimization algorithm 333</p>
<p>13.3. Adapting an evolutionary algorithm to the interactive fitting of cochlear implants 335</p>
<p>13.4. Evaluation 338</p>
<p>13.5. Experiments 339</p>
<p>13.6. Medical issues which were raised during the experiments 350</p>
<p>13.7. Algorithmic conclusions for patient A 352</p>
<p>13.8. Conclusion 354</p>
<p>13.9. Bibliography 354</p>
<p>List of Authors 357</p>
<p>Index 359</p>
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