|
|
 |
| Авторизация |
|
|
|
| Поиск по указателям |
|
|
|
|
|
|
|
 |
|
|
|
| Bow S.-T. — Pattern recognition and image preprocessing |
|
|
|
| Предметный указатель |
Kronecker delta functions 84
Lagrange multipliers 170—171
Laplacian masks 311
Laplacian operator 306—311
Layered machines 46—48
Likelihood function 86 161 349
Limited neighborhood concept 155
Limited neighborhood sets 155
Linear dichotomies 54—57
Linear discriminant functions 34—38 96—97
Linear separable classes 43
Logarithmic Laplacian edge detector 325
Logic circuit diagram reader 523 526—528
Loop 149
Loss function 83
Loss matrix 84
Low-pass filtering 468—471
Low-pass filtering, Butterworth 468—471
Low-pass filtering, exponential 468—171
Low-pass filtering, ideal 468—471
Low-pass filtering, trapezoidal 468—471
MADALINE 46 201
Mahalanobis distance 92 94 96 114 169
Main diameter 150 167
Match count 155
Matrices 613
Matrices, inverse matrix 617—619
Matrices, matrix multiplication 614—615
Matrices, partitioning of matrices 615—616
Maximal spanning tree 152
Maximum distance algorithm 119—121 141—142
Maximum likelihood decision 86
Maximum likelihood rule 87
Maxnet 237—238
Mean vector 94 168
Measure of similarity 19 94
Measurement space 168
Medial-axis transformation 334 378—380
Membership boundary 118
Merging function 188
Mexican Hat function 249
MFLOPS 563
Minimal spanning tree 149 151 366
Minimal spanning tree method 149 166
Minimization of sum of squared distance (K-mans algorithm) 129—131 138 164
Minimum distance classifiers 40 98
Minimum squared error procedure 76
Minimum squared error solution 76
Minkowski addition 336
Minkowski subtraction 339
Mixture features 188
Mixture statistics 161—164
Modes 161
Morphological processing 336—343
Multicenters 142—144
Multilayer perceptron 198 201 205—206
Multiprototypes 40
Multiresolution 482
Multiresolution, pyramid decomposition 497—498
Multiresolution, reconstruction structure 502
Multispectral data 13
Multispectral scanner (MSS) 7 15—16 18
Multivariate Gaussian data 168
Multivariate Gaussian distribution 33 168
Multivariate normal density 95 98
Natural association 19 32
Nearest neighbor classification 122
Nearest-neighbor rule 42
Negative loss function 84
Neighborhood average algorithm 300
Neighborhood processing 272 401
network topologies 567
Network topologies, 3-cube 567
Network topologies, 3-cube-connected cycle 567
Network topologies, chordal ring 567
Network topologies, linear array 567
Network topologies, near-neighbor mesh 567
network topologies, ring 567
network topologies, star 567
Network topologies, systolic array 567
network topologies, tree 567
Neural network models 5
neuron 197
Nonhierarchical clustering 142
Nonlinear discriminant function 34 49—52
Nonparametric decision theoretic classification 33
Nonparametric feature selection, application to mixed feature 188
Nonparametric pattern recognition 33
Nonparametric training of discriminant function 62
Nonsupervised learning 112
Normal density function 94
Normal distributed patterns 93
Normal distribution 93
Numerical integration 591—593
Numerical taxonomy 31
Opening operation 342—343
Optical spectrum 13
Optimal acquisition of ground information 545—551
Optimum discriminant function 82 89
Orthogonal matrix 405
Orthonormal functions 58 102
Orthonormal transformation 162
Orthonormal vector 468
Orthonormality 405
Panning 601
Parametric pattern recognition approach 33
Partition 31
Path 149
Pattern 3—4 16
Pattern class 3
Pattern mappings 205
Pattern recognition 3—4 30 42
Pattern recognition, pattern recognition system 5—9
Pattern recognition, pattern recognition technique 4 6—7
Pattern recognition, supervised pattern recognition 5 32
Pattern recognition, three phases in pattern recognition 8
Pattern recognition, unsupervised pattern recognition 5 32
Pattern space 8 16 32 34 62
Pattern vector 16
Perception criterion function 73
Perceptron training algorithm 73
Perceptrons 73 201
Phoneme 25
Phoneme recognition 25
Pictophonemes 22
Piecewise linear discriminant functions 34 42
Point processing 272—273
Point spread function 586
Potential functions 57
Pragmatics 112
Primitives 21—24
Principal component analysis 172
Principal component axis 177
Principal component axis, principal component axis classifier design 177
Principal component axis, procedure for finding the principal component axis 178
Probability density functions 57 98 101
Probability distributions 280—286
| Probability of error 90—93
Prototype average (or class center) 38
prototypes 19 29 34 40—42 44 58 63 70
Pseudoinverse method (technique) 76—77
Quadratic decision surface 51
Quadratic discriminant function 52 96—97
Quadratic processor 52
Quadtree 363—370
Quantization 597—599
Quantization, tapered 599
Quantization, uniform 598
Radial basis function networks (RBF) 225—231
Radial basis function networks (RBF), comparison of RBF with MLP 234
Radial basis function networks (RBF), formulation of RBF by means of statistical decision theory 232—234
Radial basis function networks (RBF), RBF training 231—232
Region of influence 156—158
Relative neighborhood graph 156—158
Relaxation algorithm 74
Relaxation criterion function 74
Risk function 83
Robert's cross operator 304
Robotic vehicle road following 552—559
Sampling 420 429 431 597—599
Sampling device 437
Sampling device, annular-ring 437
Sampling device, wedge-shaped 437
Scaling 601
Scaling function 486 489
Scaling function, Haar scaling function 489 491
Scaling function, scaling function coefficients 487
Scaling function, two-dimensional scaling function 499
Scatterogram 556
Semantics 112
Separating surfaces 34—36
Sequential learning approach 348—351
Sgn (signum function) 260
Share near neighbor rule 152
Shared near neighbor maximal spanning tree 152—155
Short time Fourier transform (STFT) 482—484
Sigmoid logistic nonlinearity 49 205
Similarity function 140
Similarity matrix 146—149
Similarity measure 113—114
Sine function 408
Smoothing 274—303
Sobel operator 311—312
Solution region 65
Solution weight vector 67 80
Space: classification space 8
Space: feature space 8
Space: pattern space 8
Spanning tree 149
Spanning tree method 149
Spanning tree method, graph theoretic clustering based on limited neighborhood sets 155—161
Spanning tree method, maximal spanning tree for clustering 152—155
Spanning tree method, minimal spanning tree method 149—152
Spatial domain 401—402 422 429
Spatial processing 271
spatial resolution 598
Spectral band 13
Spectral characteristics 11
Spectral distribution: of the scaling function 503 505
Spectral distribution: of wavelets 503 505
Spectral range 104—106
Spectral response 15
Spectrum 407—410 413—417 426—440
Speech recognition 24
standard deviations 102 131 137 139 172 199 329
State conditional probability density function 83
Statistical decision method 83
Statistical decision theory 83
Statistical differencing 305
Statistical discriminant functions 82
Statistical discriminant functions, training for statistical discriminant 101
Steepest descent 72
Structural pattern recognition 21
Subpatterns 20
Successive doubling, method of 442—451
Sum of squared error criterion function 77
Supervised learning (supervised recognition) 29—30 33 112
Symmetrical loss function 83—84 87 91
Synaptic weights 29
Syntactic pattern recognition 20—23
Syntax 112
Syntax rule 20
Tanimoto coefficient 114
Template matching 419
text strings 513
Texture and textual images 352—354
Texture features 27
Thinning 333—336
Threshold logic unit (TLU) 46
Traffic flow measuring 551—552
Training decision processor 17
Training set 83
Transform domain 271—272
Transform processing 401—476
Transformation matrix 173 601
Translation 412—416 603
TREE 149
Two-dimensional Fourier transform 406—409
Typology 31
UNION operator 112
Unitary matrix 405
Unsupervised learning 29—32
Upsampling 494—495
Variables spectral 580
Variables temporal 580
Variables: spatial 580
Variance 40 92 116
Vector gradient 303
Vector space: of signals S 486
Vector space: spanned by the scaling function 486 488
Vector space: spanned by the wavelet function 487—488
Wallis operator 325—326
Walsh transform pair 457
Walsh transformation 454—459
Wavelet 481—501
Wavelet transform 481—497
Wavelet transform, continuous 484—485
Wavelet transform, discrete 484—485 494—499
Wavelet transform, inverse discrete 484 494
Wavelet transform, two-dimensional 499
Wavelet, analysis 483
Wavelet, coefficient 484—485 494—495 497
Wavelet, functions 485
Weather forecasting 23
Weight adjustments 207
Weight adjustments, in backward direction 207
Weight array 300
Weight coefficient 114
Weight Euclidean distance 113—114
Weight matrix 98
Weight space 62—63 69
Weight vector 62 70
Weight, of a tree 149
Widrow — Hoff rule 79
|
|
|
| Реклама |
|
|
|
|
|
О проекте