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

$\phi$ functions      52
$\phi$ machines      52—54
$\phi$ space      52
A posteriori probability      83
a priori probability      83—85
Absolute correction rule      67 69—71
ADALINE      46 201
Adaptive decision procedure      17
Adjoint      99 616
Agglomerative approach      114
Aperture      424—431
Aperture function      423—424
Approximations      492 499—500
Approximations, successive approximation      494
Area of high activity      302
Area of low activity      302
Artificial neural networks (ANN)      197—199
Augmented feature space      38
Augmented pattern vector      35 39
Augmented weight vector      35
Autocorrelation      416
Average cost      83
Average risk      83
Bachelor and Wilkins, algorithm      119—121
Back-propagation algorithm      211—219
Back-propagation algorithm, flowchart for      220
Basic events      188
Basic events, basic event generation algorithm      188—190
Bayes decision boundary      98 106
Bayes decision surface      106 109
Bayes discriminant function      85—86
Bayes' classifier      86
Bayes' law (rule)      85—86 91
Bit reversal      448—449
Blurring      238—239 302
Butterworth filter      469—473
Canonical analysis      172
Capacity of a $\phi$ machine      54—57
Carpenter — Grossberg classifier      198—199
cell      12
Cell, acidophilic      12
Cell, cosinophilic      12
Chain cluster      157
Chain code      374—375
Character recognition      24
Classification      18
Classification space      8
Classifier      20 23
Closing operation      342—343
Cluster      32
Cluster center      117—118 132—138
Cluster domain      129—139 145
Clustering analysis      112
Clustering method, evaluation of the cluster methods      145
Clustering transformation      170
Cofactor      615
Collinear string      516
Committees machines      46
Committees machines, training procedure for committee machines      74
Compass gradient mask      307
Concept of saliency of a boundary segment      394
Conceptual recognition      5
Conditional average loss      83
Conditional average risk      83 85
Connected component generation      514
Connected graph      149
Connectionist model      197
Connectivity      343
Contour map      24
Contour tracing      344—345
Convex decision surface      43
Convexity      43
Convolution      306 336 401 416—419 587—589
Convolution integral      587—589
Convolution theorem      419
Cooccurrence matrix      352—354
Correlation      416—419
Correlation method      24 416—419
Cost function      31
Covariance      33 180
Covariance matrix      97
Covariance matrix, between-class covariance matrix      184
Covariance matrix, within-class covariance matrix      184
Criterion function: perceptron criterion function      73
Criterion function: relaxation criterion function      74
Cross-correlation      416
Cumulative density function      286—288
Cumulative distribution      286—288
Cumulative histogram      286—288
Curve fitting      374
Curve fitting, B-spline      374 377—378
Curve fitting, concatenated arc approximation      374 385—392
Curve fitting, piecewise polynomial      374
Curve fitting, polygonal approximation      374—376
Curve fitting, polynomial      374—376
Data: acquisitions      9
Data: preprocessing      10 18
Data: reduction      10
Decimation      492
Decision boundary      91
Decision classification      20
Decision classifier      19
Decision function      19 57
Decision processor      17 19
Decision processor, training of decision processor      17 19
Decision surface      33—36 39—40 51 61
Decision theoretical approach      35 37
Degree of similarity      143 146
Delaunay method      366 372
details      492
Deterministic function      34
Deterministic gray-level transformation      271
Diagonal matrix      173 176 467
Dichotomies      52 56
Dichotomies, probability of dichotomies      55
Dichotomization      54
Dichotomization, capacity of dichotomization      57
Differential operators      590—591
Digital image processing      268
Digital implementation of DWT      496
Dilation      336—339
Dimensionality reduction      172
Dirac delta function      405 584—586
Discrete Fourier Transform      401 403 422 441—447
Discrete Karhunen — Loeve transform      466—468
Discriminant functions      33—39 42 48—49 52 57 88 96
Discriminant functions, nonparametric training of discriminant function      62
dispersion      140
Dissimilarity measure      113
Distance function      20
Distance measure      118—119 138—144
Distance metrics      20
Divisive approach      114
Document image analysis      513—523
Downsampling      492—493 495
Dynamic cluster method      142—144
Dynamic optimal cluster seeking technique (DYNOC)      141—142
Ecology      31
EDGE      149 158
Edge sharpening      303—330
Edge weight plots      152—153
Eigenvalues      51—52 94 162 175—176 468 621—623
Eigenvectors      52 467
Energy spectrum      407
Erosion      339—341
Error-correcting training methods      66 76
Euclidean distance      113
Euclidean distance, classifier      38
Euclidean distance, of unknown pattern from class center      38
Euclidean space      20 62

Expected value      92
Exponential filter      469—471
Fast Fourier Transform      401 437 446—453
Fast Walsh transform      459
Feature extraction      8 10 14 18
Feature ordering      11 170
Feature selection      168 188
Feature space      8 18—20 168
Feature subset      188—189
Feature vector      11 14 17 20 32
Feature weighting coefficients      170—171
features      10—11 30
Features, derived feature      15
Features, global feature      11
Features, local feature      11
Filter      469—471
Filter banks      490 492—493
Filter, Highpass      469—471
Filter, Lowpass      469—471
Finite differences      593—595
Fisher's criterion function      185
Fisher's determinant      182 187
Fixed increment rule      67 71
flops      563
Forward transform      405—496
Forward transform kernel      405—410
Fourier coefficient      483
Fourier descriptor      380—384
Fourier spectrum      407—417 424—440 482
Fourier transform      404—454
Fourier transform, Fourier transform pair      404—419
Fourier transform, windowed Fourier transform      482
Fractional correction rule      68—71
Frame grabbing time      596
Frequency domain      420
Frequency spectrum      407—417 424—440
Functional approximation      101
Fuzzy decision      199
Fuzzy membership function      113
Gabriel graph      157—161
Gaussian distribution      82
Generalized decision function      55—56
Generalized inverse      77
Generalized likelihood ratio      87
Generalized threshold value      87
Gradient      304
Gradient descent technique      72
Gradient technique      72
Grammatical inference      22
Graph theoretical method      146
Graphics description      513—517
Graphics understanding      517
Gray level distribution      103
Gray level transformation      271—295
Gray level transformation functions      272
Gray level transformation matrix      296
Gray-tone run length      352
Ground truth      103
Hadamard matrix      460
Hadamard transform      459—466 481
Hadamard transformation pair      462
Hamming distance      237
Hamming net      198—199 236—240
Hamming net classifier, algorithm for      238—246
Hamming net, example      241—246
Hard limiter      201
Hermite polynomials      101
Heuristic approach      114 117
Hidden layer      206—207
Hierarchical clustering algorithm      121—128
High-pass filtering      471—473
High-pass filtering, Butterworth      471—473
High-pass filtering, exponential      471—473
High-pass filtering, ideal      471—473
High-pass filtering, trapezoidal      471—473
histogram      103—106
Histogram equalization      288—290
Histogram modification      278 287
Histogram specification      291 295
Histogram thinning      295—298
Ho — Kashyap algorithm      77—78
Homogeneous coordinates      600—601
Hopfield net      198—199 256—261
Hopfield net, architecture of the Hopfield net      257
Hopfield net, operation of Hopfield net      261—265
Hough transform      346—348
Hyperplane      34 36 63—64 69
Identification of: industrial parts with x-ray      542—545
Identification of: partially obscured objects      392—399
Identification of: scratches and cracks on polished crystal blanks      534—542
Identification of: scratches and cracks on unpolished crystal blanks      530—535
Identity matrix      51 175 460
ideographs      22
Image: acquisition      8
Image: cytological      12
Image: data preprocessing      8
Image: display      8
Image: enhancement      271 473—476
Image: function      271 303 412 441 453 581—582
Image: information content      440
Image: inverse transform      401
Image: model      402—403 579
Image: plane      600—601 607
Image: processing      402
Image: segmentation      8
Image: spectrum      401
Image: transform      401 403
Impulse      421
Impulse sheet      422
Indeterminate region      45—46
Inertia function      113
Interconnection networks      566—571
Interconnection networks, MIMD      566
Interconnection networks, SIMD      566
Interpolation      494
Intersample distance      146
Intersection      112
Interset distance      32 115
Intraset distance      32 115—116
Inverse fast Fourier transform      401
Inverse Fourier transform      411 422
Inverse Hadamard transform      462
Inverse transform      407—408 419 441
Inverse transform kernel      405 441
Inverse Walsh transform      455
ISODATA algorithm      131—139
ISODATA algorithm modification of ISODATA      139—141
K-means algorithm      129—131
k-nearest neighbor decision rule      121—128
k-nearest neighbor decision rule, definition of $B_{k}^{m,n}$ and $B_{k}^{n,m}$       127
k-nearest neighbor decision rule, definition of $P_{k}(x_{i})$       123—124
k-nearest neighbor decision rule, definition of $P_{k}^{sc}(m)$       125
k-nearest neighbor decision rule, definition of $P_{k}^{sc}(n)$       125
k-nearest neighbor decision rule, definition of $SIM_{1}(m, n)$       125—127
k-nearest neighbor decision rule, definition of $SIM_{2}(m, n)$       125—127
k-nearest neighbor decision rule, definition of $Y_{k}^{m,n}$ and $Y_{k}^{n,m}$       126
k-nearest neighbor decision rule, definition of $\Omega_{k}(x_{i})$       123—124
k-nearest neighbor decision rule, definition of $\zeta_{k}(x_{i})$       123—124
k-nearest neighbor decision rule, definition of SIM(m, n)      125—127
Karhunen — Loeve method      24
Kernel: forward transform      441—442
Kernel: Fourier transform      405
Kernel: Hadamard transformation      459—166
Kernel: inverse transform      441
Kernel: separability      409—410
Kernel: Walsh inverse transformation      455
Kernel: Walsh transformation      454—459
Kirsch edge detector      312 325
Kohonen self-organizing feature maps      198—199 246—151
Kohonen, SOFM algorithm      251—252

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