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Amit Y. — 2D object detection and recognition
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Название: 2D object detection and recognition
Автор: 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.
The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse to fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.
Yali Amit is Professor of Statistics and Computer Science at the University of Chicago.
Язык:
Рубрика: Computer science /Обработка изображений /
Статус предметного указателя: Готов указатель с номерами страниц
ed2k: ed2k stats
Год издания: 2002
Количество страниц: 306
Добавлена в каталог: 25.11.2005
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Предметный указатель
Absolute arrangements 26 184 196 201—208 219 230
Aggregate classifier 190 192 202—205
anchor points 123 163
Angiogram 76 80
Area integral 39
Arrangement of local features 26 184
Arrangement of local features, absolute 181 see
Arrangement of local features, constraints 113 151
Arrangement of local features, relational 181 see
Arrangement of local features, star type 184
Backward transform 41 55 89
Backward transform, discrete 42 90 91
Basis coefficients 33 42 85 86 88
Basis functions 32 33 38 41 42 45 53 85 105
Basis functions, Fourier 33 see
Basis functions, linear 92
Basis functions, principal components 54
Basis functions, wavelets 33 see
Bayes classifier 8
Bayesian modeling x 11 18
Bayes’ rule x 17
Binomial distribution 68 128 135
Boosting 191 192 203—205
Bottom-up processing 1 7 215
Brain 101 173
Brain, activity 101
Brain, matching 101
Brain, ventricle 109—111 120
Brute force search 153 154 156
Chess piece 45 216
Chess piece, classification 216
Cholesky decomposition 98
Classification tree x 25 185 186 188 196 215 230 236
Classification tree, depth 185 187 203
Classification tree, multiple 181 see
Classification tree, node empirical distribution 186
Classification tree, predictors 25
Classification tree, purity measure 186
Classification tree, query 185 186 189
Classification tree, recursive partitioning 26
Classification tree, relational arrangements 198
Classification tree, split 186 200
Classification tree, stopping rule 186 203
Classification tree, terminal node 186 187
Classification tree, terminal node, class distribution 187
Classification tree, terminal node, class distribution estimates 187
Classification tree, testing 187
Classification tree, training 26 185 186 200
Clutter 45 57 64 76 79 207 208 219
Coarse to fine, computation 34 45 53 87 89 93 99 145 180
Coarse to fine, object model 180
Coarse to fine, sparse model 145
Comparison arrays 110 118 119
Compositional models 11
Computer vision 1 2 40
Conditional independence 16 36 53 57 61 69 72 95 115 128
Conjugate gradient 45 91
Continuum 57 84 87
Continuum formulation 32 36 37 49 53 81
Correspondence space search 5 148
Cost function 18 57 112
Cost function, deformable contour 37
Cost function, deformable image 95
Cost function, non-linear 41 107
Covariance matrix 54
Data model, deformable contour 35 48
Data model, deformable curve 59 68
Data model, deformable image 84 93
Data model, sparse model 114
Daubechies wavelet 33 100
Decomposability 140
Deformable contour 4 19 40 51 53 57 78 88 179
Deformable contour, algorithm 42 46
Deformable contour, coarse to fine 45 47 53 89
Deformable contour, computation 41
Deformable contour, cost function 37
Deformable contour, data model 35 37 48
Deformable contour, deformations 31 32 34 54 88
Deformable contour, detection 169
Deformable contour, discretization 42
Deformable contour, edge model 40 53
Deformable contour, initialization 79
Deformable contour, inside-outside model 31 36 55 88
Deformable contour, instantiation 32 42 48 54
Deformable contour, lattice parameterization 53
Deformable contour, likelihood 36 37
Deformable contour, parameter estimation, off-line 48 52
Deformable contour, parameter estimation, on-line 48 51 52
Deformable contour, posterior 35 36 48 49
Deformable contour, prior 32 35
Deformable contour, shape 31 174
Deformable contour, sparse model initialization 169 171 174
Deformable contour, spectral parameterization 33 53
Deformable contour, template 32 79
Deformable contour, time step 42 44
Deformable contour, variational analysis 37
Deformable curve 4 20 78 116 179
Deformable curve, algorithm, dynamic programming 63—66 78 80
Deformable curve, algorithm, tree based 67 74 78 80
Deformable curve, background model 59 68
Deformable curve, backtracking 76
Deformable curve, computation time 64
Deformable curve, data model 57 59 61 62 68
Deformable curve, deformations 57
Deformable curve, detection 169
Deformable curve, image transform 58
Deformable curve, initialization 79 80
Deformable curve, instantiation 57 60 62 67 79
Deformable curve, jump ahead 76
Deformable curve, likelihood 59—61 68
Deformable curve, local features 58
Deformable curve, model 62 63
Deformable curve, parameter estimation 61
Deformable curve, posterior 62 71
Deformable curve, posterior, partial 71 73 74
Deformable curve, prior 57 62 80
Deformable curve, prior, tree structured 67
Deformable curve, shape 67
Deformable curve, template 57 62 79
Deformable image 21 101 105 179
Deformable image, algorithm 101
Deformable image, algorithm, coarse to fine 89 99 101 104
Deformable image, Bernoulli model 4 85 93 97 105 112 121 168 179
Deformable image, Bernoulli model, background 96
Deformable image, Bernoulli model, image transform 94
Deformable image, computation time 100
Deformable image, cost function 87 88 95
Deformable image, cost function, linearization 92 97 98 100 101
Deformable image, deformations 81—83 87 88 93 95 101 105
Deformable image, discretization 90
Deformable image, displacement field 84 85 87 93 99
Deformable image, flow models 104
Deformable image, Gaussian model 84 97 112
Deformable image, image transform 85
Deformable image, initialization 92
Deformable image, instantiation 84 95 96
Deformable image, lattice parameterization 92 100 104
Deformable image, likelihood 84 87 95
Deformable image, parameter estimation 85 96 105
Deformable image, pose parameters 92
Deformable image, posterior 87 95
Deformable image, prior 85 87
Deformable image, prototype image 84 88 96 104
Deformable image, regularizing term 87
Deformable image, sparse model initialization 168 180
Deformable image, spectral parameterization 85 87 93 98 104
Deformable image, template 82
Deformable image, time step 91
Deformable image, training 96
Deformable models x 3 6 19 24 111
Deformable models, automatic initialization 163 166
Deformable models, instantiation 161
Deformable models, sparse model initialization 180
Deformable models, user initialization 19
Deformations, deformable contour 31 32 54
Deformations, deformable curve 57
Deformations, deformable image 81—83 87 88 93 95 101 105
Dynamic programming 17 57 63 67 117 140 151
Dynamic programming, deformable curve 63 148
Dynamic programming, sparse model 148
Dynamic programming, state space 63 142 148 149
Edge arrangements 7 113 121 122 125 128 157 163 184 194 206 216 221 224
Edge arrangements, background density 129—131 133
Edge arrangements, complexity 121 129—131
Edge arrangements, subregions 121 129
Edge arrangements, two-edge arrangements 123 194 236 240
Edge arrangements, wedges 121 122 128
Edge maps 128 161 162
Edges 93 94 113 121 184 194 219 236
Edges, background density 129—131 136 160
entropy 69 72
Entropy, conditional 70 186
Entropy, joint 69
Euler equations 100
Face 128 132 162 163 168
Face, deformations 81 82
Face, detection 97 125
Face, detector 161—163
Face, edge arrangements frequencies 125
Face, edges frequencies 125
Face, instantiation 96
Face, matching 82
Face, sparse model 125 126 155
Fast Fourier Transform 42
Feed forward neural net 185 196 253
Fisher 185
Forward transform 38 41 50 55 89 95
Forward transform, discrete 42 90 91
Fourier basis 35 42 86 87
Gaussian 35 37 48 52 84 85 93
Generative models 11
Geometric invariance 10 26 118 122 184 193 212 241
Geons 178
Global optimization 57
Global optimum 55 79
Gradient descent 17 31 41 57 84 88 89 92 95 99 100 112
Gradient flow 38 41 91
Green’s Theorem 39 50
Handwritten digits 181 202 233
Heart 51
Heart ventricle 46
Hebbian learning 29 237 241 245 253 256
Hebbian learning, field dependent 244 245 253 257
hessian 44 91 100
High-level processing 1
Homeomorphisms 104
Hopfield networks 257
Hough transform 6 153—155
Hypothesis 128
Image compression 93
Image deformation 81
Image grid 13
Image normalization 9
Image registration 9
Image segmentation 1 7 11 27 53 181 215 227
Image sequence analysis 4 100
Image surface 20 41 81
Image surface, local topography 14 109 120
Image surface, topography 81 85
Image synthesis 5 111
Image transforms 4 16 18 21
images, background 129 135 161
Images, office 136
Inexact consistent labeling 6 148
Initialization 31 55
Initialization, deformable contour 79
Initialization, deformable curve 78 79
Initialization, deformable image 107
Instantiation, deformable contour 32
Instantiation, deformable curve 57 79
Instantiation, deformable image 95 96
Instantiation, region of interest 161 162 181
Instantiation, region of interest, registration 161
Instantiation, sparse model 112 116
Interpolation 160
Interpolation, linear 90
Laplacian 100
LATEX symbols 201 206 247
LATEX symbols, detection 226
LATEX symbols, prototype 168
LATEX symbols, random deformations 168
LATEX symbols, recognition 226
LATEX symbols, scene analysis 224—226
LATEX symbols, sparse model 168
LATEX symbols, sparse model, detection 170
LATEX symbols, sparse model, local features 168
LATEX symbols, sparse model, training 168
Least squares 98 100
Level curves 81
Level set methods 54
Likelihood, deformable contour 36 37
Likelihood, deformable curve 59
Likelihood, deformable image 87
Likelihood, ratio 60
Likelihood, sparse model 114 115
Linear discriminant analysis 185 196
Local features 16 20 24 93 112 184 221
Local features, background density 111 128 129 135—137 145 161
Local features, background probabilities 115 118
Local features, binary 16 21
Local features, clustering 158
Local features, comparison arrays 118 see
Local features, consistent arrangement 5
Local features, density 149
Local features, edge arrangements 121 see
Local features, edges 93 see
Local features, false positives 109
Local features, invariant 112
Local features, micro-image codes 184 see
Local features, on class probability 246
Local features, pose invariance 133
Local features, registered 216 217 219 221
Local features, ridges, see ridges 93
Local features, spreading 193 196 201 206 219 241
Local features, statistics 111 128 245
Low-level processing 1
Machine learning 212
Maximum likelihood 48 61
Mean curvature 41
Medical imaging 31
Micro-image codes 193 202
Minimal cut 55
Model shifting 233
Motion estimation 93 104
MPEG 93
MRI 31 101 109
MRI, brain scan 48 58 65 66 76 77 102 106 109 110 144 147
MRI, brain scan, instantiation 174
MRI, brain scan, sparse model 146 173 174
MRI, brain scan, ventricle 109
MRI, functional 101
Multiple classification trees 27 165 189 202 216 217 225
Multiple classification trees with absolute arrangements 196
Multiple classification trees with relational arrangements 198
Multiple classification trees, aggregation 189—191 202
Multiple classification trees, boosting 191 192
Multiple classification trees, boosting, overfit 204
Multiple classification trees, conditional covariance 209
Multiple classification trees, conditional independence 209
Multiple classification trees, experiments 201
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