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Amit Y. — 2D object detection and recognition
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Название: 2D object detection and recognitionАвтор: Amit Y. Аннотация: Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency.
Язык: Рубрика: Computer science /Обработка изображений /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Год издания: 2002Количество страниц: 306Добавлена в каталог: 25.11.2005Операции: Положить на полку |
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Multiple classification trees, mean margin 210 Multiple classification trees, object recognition 192 Multiple classification trees, randomized 165 185 187 189 197 219 247 multiple objects 116 Mutual information 71—73 106 Network x 12 28 Network, abstract module 238 256 Network, abstract module, class subset 238 Network, architecture 235 255 Network, biological analogies 252 Network, bottom-up processing 254 Network, classification 12 29 248 Network, detection x 12 28 252 Network, detection layer 248—250 Network, gating 250 254 255 Network, Hebbian learning 241 see Network, inhibitory units 241 250 Network, input, high level 240 Network, input, low level 240 Network, input, visual 236 Network, invariant detection 254 Network, layers 235 Network, learning 12 28 253 256 Network, learning, classifier 241 Network, learning, object model 238 240 Network, location selection 250 252 Network, location selection, bottom-up 251 252 Network, location selection, pop-out 252 Network, location selection, top-down 249 251 Network, module 238 Network, priming 248 250—252 Network, recognition x 250 252 Network, recognition, off center 250 Network, retinotopic layers 236 248 Network, top-down information flow 249 251 254 255 Network, training 239 244 Network, translation 251 252 Network, translation layer 250 Neural dynamics 236 Neural system 235 236 Neuron, afferent connections 234 Neuron, afferent units 240 Neuron, binary 234 Neuron, local field 234 244 Neuron, output 234 Neuron, post-synaptic 237—239 244 257 Neuron, pre-synaptic 234 236—239 257 Neuron, threshold 234 235 NIST database 201 228 244 NIST database, misclassified digits 202 NIST database, pre-processing 201 Non-linear deformations 19 158 Normal equations 98 Object boundary 31 81 Object cluster 27 215 216 228 230 251 Object clustering 228 Object clustering, sequential 229 Object clustering, tree based 230 object detection ix 3 11 18 215 219 Object detection and recognition 7 27 215 219 220 221 229 Object detection as classification 25 Object detection, Bayesian approach 13 Object detection, deformable contour 178 see Object detection, deformable curve 178 see Object detection, deformable image 178 see Object detection, model points 14 Object detection, non-rigid 2d 3 7 8 Object detection, rigid 3d 5 7 178 Object detection, rigid 3d, 3d models 178 Object detection, rigid 3d, sparse model 171 172 Object detection, rigid 3d, view based 230 Object detection, rigid 3d, view based models 8 171 178 Object detection, sparse model 178 see Object model 2 241 Object model, admissible instantiation 15 18 Object model, coarse to fine 180 Object model, complexity 14 Object model, computation 17 18 Object model, cost function 17 Object model, data model 16 Object model, efficient computation 17 Object model, image transforms 16 18 Object model, instantiation 14—17 Object model, learning 241 Object model, likelihood 16—18 Object model, model points 13 14 18 Object model, one dimensional 31 81 88 107 180 Object model, parameter estimation 18 Object model, posterior 16 17 Object model, prior 15 17 18 Object model, sparse 109 Object model, template 3 13 15 18 179 Object model, two dimensional 88 107 180 Object pose 96 Object recognition ix x 8 11 25 181 215 219 Object recognition, deformable models 8 Object recognition, local features 193 194 Object recognition, multiple classification trees 192 Occam’s razor 18 Occlusion 17 23 113 151 159 Olivetti data set 163 Optical flow 100 or-ing 10 12 113 121 193 196 256 Parameter estimation, deformable contour 48 Parameter estimation, deformable curve 61 Parameter estimation, deformable image 96 Parameter estimation, sparse model 119 122 PARTS 214 232 253 Pattern recognition 212 Peeling 141 142 Perceptions 247 Perceptions, multiple randomized 247 256 Perceptions, multiple randomized, voting 247 Photometric invariance 4 10 16 20 58 93 94 105 113 118 120 184 193 Pose space search 6 Pose space search, coarse to fine 6 Positron emission tomography 101 Posterior, deformable contour 35 48 Posterior, deformable curve 62 Posterior, deformable image 87 95 Posterior, sparse model 114—116 pq probabilities 243 Predictors 185—188 193 196 Predictors, random subset 186 189 Prefrontal cortex 254 Priming 254 Principal components 35 54 106 Prior, deformable contour 32 Prior, deformable curve 57 62 Prior, deformable image 87 Prior, sparse model 114 139 Prototype image 14 17 21 82—84 93 111 133 216 qr 98 Quasi-Newton 92 100 Recurrent connections 235 Reference grid 13 57 96 111 113 160—162 173 238 Reference points 123 125 Region growing 53 Region of interest 215 221 Relational arrangements 26 184 197—208 Relational arrangements, as labeled graph 198 Relational arrangements, as query 199 Relational arrangements, instances 198 200 Relational arrangements, minimal extension 199 Relational arrangements, partial ordering 197 198 Relational arrangements, pending 199 200 Ridges 58 93 Road tracking 78 Rotation invariance 133 139 213 Saccade 250 Scale invariance 62 139 144 196 201 Scene 13 Scene analysis x 7 10 27 215 228 Scene interpretations 229 Serial computation 233 SHAPE 2 45 48 53 54 81 Shape classification 184 Smoothness penalty 87 Sparse model 4 7 21 23 24 111—113 179 215—217 224 228 229 248 Sparse model, admissible instantiation 117 135 151 Sparse model, as initialization 163 Sparse model, candidate centers 117 151 154 156 160 249 Sparse model, candidate centers, density 159 Sparse model, coarse to fine 145 151 153 Sparse model, computation time 148 153 160 179 Sparse model, counting detector 23 28 153 155 159 163 172 184 248 Sparse model, counting detector, step I 23 154 157 159 161 164 169 248 Sparse model, counting detector, step II 23 157 159 160 163 164 166 169 Sparse model, data model 114 Sparse model, detection 152 163 251 Sparse model, dynamic programming 6 23 142—145 148 Sparse model, false negative probability 135 Sparse model, false positive density 128 135—137 159 Sparse model, false positives 152 157 Sparse model, final classifier 161 165 169 Sparse model, image transform 114 Sparse model, instantiation 112 114 116 128 135 145 147 157 158 160 200 Sparse model, instantiation, clustering 158 Sparse model, landmarks 109 119 122 Sparse model, landmarks, user defined 109 Sparse model, likelihood 114 115 Sparse model, local features 113 117 140 151 157 220 Sparse model, local features, consistent arrangement 111—113 151 152 184 Sparse model, local features, on object probabilities 114 128 129 131—134 153 Sparse model, multiple objects 116 Sparse model, parameter estimation 119 122 Sparse model, pose detection 147 156 168 215 217 Sparse model, posterior 114—117 135 Sparse model, prior 114 139 Sparse model, prior, decomposable 23 140 Sparse model, template 113 119 Sparse model, threshold 117 126 128 Sparse model, training 119 122 157 224 240 Sparse model, training, edge arrangements 124 Splines 35 Statistical model 40 48 53 54 104 Statistical modeling 18 Support vector machine 185 Synapse 234 Synapse, depression 238 242 Synapse, efficacy 234—238 240 242 244 248 Synapse, internal state 237—239 241 244 Synapse, potentiation 238 241 244 253 Synaptic connections 235 Synaptic connections, directed 235 Synaptic modification 237 Template, deformable contour 32 81 Template, deformable curve 57 81 Template, deformable image 82 Template, sparse model 119 Test error rate 187 Thin plate splines 160 Tracking in time 54 Training error rate 187 Translation invariance 196 201 Ultrasound 46 Unsupervised learning 188 Unsupervised tree 188 Unsupervised tree, class distribution estimates 188 User initialization 3 4 57 109 149 USPS database 202 228 Ventricles 45 Visual scene 233 Visual system 7 8 233 234 250 252 253 Visual system, complex cells 253 Visual system, cortical column 253 Visual system, infero-temporal cortex 254 Visual system, layers 253 254 Visual system, object detection 234 Visual system, object recognition 234 Visual system, orientation selectivity 253 Visual system, receptive field 253 Wavelet basis 33 35 42 45 86 87 100 Wavelet basis, Daubechies 33 Wavelet basis, discrete transform 34 42 43 90 Wavelet basis, packets 35 87 106 Wavelet basis, pyramid 33 86 Wavelet basis, resolution 34 35 86 Wavelet basis, two dimensional 86 Wavelet basis, two dimensional, discrete transform 90 Weighted training sample 191
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