Bow S.-T. — Pattern recognition and image preprocessing :: Электронная библиотека попечительского совета мехмата МГУ

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Bow S.-T. — Pattern recognition and image preprocessing

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Название: Pattern recognition and image preprocessing

Автор: Bow S.-T.

Аннотация:

Showcasing the most influential developments, experiments and architectures impacting the digital, surveillance, automotive, industrial and medical sciences, this text tracks the evolution and advancement of CVIP technologies. It studies:
* practical 3D computer vision algorithms
* various coding methods for individual types of 3D images
* recent trends and robust algorithms for the recognition and synthesis of the human face
* explores the use of digital faces in intelligent image coding, human computer interaction, facial impression and psychological and medical applications

Язык:

Рубрика: Computer science/Обработка изображений/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: 2nd edition

Год издания: 2002

Количество страниц: 698

Добавлена в каталог: 10.11.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID

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

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