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Petrou M., Sevilla P.G. — Image Processing: Dealing with Texture
Petrou M., Sevilla P.G. — Image Processing: Dealing with Texture

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Название: Image Processing: Dealing with Texture

Авторы: Petrou M., Sevilla P.G.

Аннотация:

Techniques for the analysis of texture in digital images are essential to a range of applications in areas as diverse as robotics, defence, medicine and the geo-sciences. In biological vision, texture is an important cue allowing humans to discriminate objects. This is because the brain is able to decipher important variations in data at scales smaller than those of the viewed objects. In order to deal with texture in digital data, many techniques have been developed by image processing researchers.


Язык: en

Рубрика: Computer science/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2006

Количество страниц: 618

Добавлена в каталог: 15.11.2009

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
A Trous algorithm      516
Admissibility condition      444
Aggregate parameters      31 38—44 49—51
Albedo      3 4
Aliasing      595 597
Ambiguity function      590 591
Area fraction      31
Associative process      64 68
Associativity      68
Autobinomial Markov random field      168 217
Autocorrelation function      161
Autocorrelation function as texture descriptor      246 250
Autocorrelation function, parametric features from      253
Autocovariance function      246 250
Autonormal Markov random field      180 219
Basis images, Laws      564—567
Basis images, Walsh      550 551 553—556 556—558 559—562
Basis, complete      465—472 542
Basis, incomplete      465 483
Basis, orthonormal      465—472 483
Battacharyya distance      535 536
Bayes theorem      212
Bayesian estimation      185
Bessel function      592
Bessel function, approximation of      592
binary number      83 575
Binomial distribution      168 182
Binomial distribution, Gaussian approximation of      174
Bit-slicing      81 83
Blanket method      100 102
Boolean model      12 40
Boolean model, 1D      44 52
Boolean model, 2D      22 23 29—31 37 38 40 41 43 44
Border pixel      31
Boundary pixel      32 34 40
Boundary, inner      32 69
Boundary, outer      69
Boustrophedon scanning      48 51 53
Box-counting method      100 102 108 109 118 119 131
Brownian motion      117 119 121 123—126
Centre of gravity      302 342 447 448
Circle, area of      33
Circle, continuous      32 33
Circle, digital      30 32—34 40—42 277-280
Circle, perimeter of      33
CLIQUE      196 197
Clique potential      198 211 218 219
Closing      54 56 57 58 60
Cluster      525
Co-occurrence matrix      275 277 281
Co-occurrence matrix of higher order      294
Co-occurrence matrix, features from      282
Coding      174 175
Coiflet wavelet      501
Commission error      536 538
Commutative operation      60 68
Commutativity      68
Complement of a binary image      68
Complement of a grey image      92 96 97
Complement of an object      68
Concentration factor      382 385 401
Confusion matrix      536 537
Connectivity      31—33 40—42 70
Consensus set      152
Continuous circle      32 33
Continuous wavelet transform      441 455 474
Continuous wavelet transform, inverse      442 456 472
contrast      282 581
Convex grain      32 35 39
Convolution      391—392
Cooling parameter      220
Cooling schedule      220
Correlation      282
Critical sampling      595
Critical temperature      196 238—245
Cross-dispersion      327 342
Daubechies wavelet      501
Delta function      305 593
Delta function, Fourier transform of      305
Delta functions, Fourier transform of train of      594
Delta functions, train of      593
Deterministic annealing      527 528
DFT      133
Difference of Gaussians filter      477
Digital circle      30 33 34 40—42 277—280
Digital square      42
Dilation, binary      5 55 58 61—66 68 90
Dilation, grey      91—95
Dimension, fractal      104 105 108 128 129 131
Dimension, Hurst      117 125 128 129
Dimension, topological      118 128
Discrete Fourier Transform      133
Discrete Fourier transform of a Gaussian      135—137
Discrete wavelet transform      474
Discrete wavelet transform, fast      489
Discrete wavelet transform, fast, inverse      494—499
dispersion      343
Distance histogram      523
distribution      24
DoG filter      477
Doppler shift      591
Dual operation      63 69
Duality      69
Dyadic wavelet      460 483 484
Eigenfunction      426
Eigenvalue problem      386 406 425
Eigenvector      386 426
Energy      282 302 342 362 368 371 375 446 452
Energy, local      346 347 363—367
entropy      282 529
Ergodicity      211
Erosion, binary      54 56 59 62 64 65 68 90
Erosion, grey      91—95
Error function      25
Error function, approximation of      26
Error, commission and omission      536 538
Error, over- and under-detection      536 538
Euler’s method of Lagrange multipliers      383
Event      38
Fast inverse wavelet transform      494—499
Fast Wavelet Transform      489
Feature map      359 432
Feature reduction      521 539 546
Feature selection      521
Feature space      523
Feature, good or bad      524
Filter, DoG (difference of Gaussians)      477
Filter, normalisation of      410 414 415 418 501
Filter, separable      423 427 428
Fourier transform      132
Fourier transform of a Gaussian      135
Fourier transform, magnitude of      262 268
Fourier transform, phase of      262 268—271 573 583 590
Fourier transform, scaling property of      436
Fourier transform, shifting property of      436
Fourier transform, texture features from      260
Fractal      104 105
Fractal dimension      104 105 116 165
Fractal dimension as texture descriptor      164
Fractal dimension from pairs of pixels      128—130
Fractal dimension from the autocorrelation, function      161—164
Fractal dimension from the box-counting, method      108 109 131
Fractal dimension from the power spectrum      127 128 146—151
Fractal surface      106 110—116
Fractal, autocorrelation of      152—154
Fractal, Fourier transform of      127
Fractal, non-deterministic      117
Fractal, power spectrum of      127
Fractal, self-affine      105 124
Fractal, self-similar      105
Fractional Brownian motion      117 119 121 123—126
Gabor function      325 329 336 341 345 454 518—520
Gabor function, choice of bands for feature, construction      362 375
Gabor function, choice of parameters for      357—360
Gaussian distribution      24 28
Gaussian Markov random field      180 219
Gaussian probability density function      24 28
Gaussian pyramid      477
Gaussian signal      303 455 585 587
Gaussian window      139—142 311—314 325 337 339 357
Geophysical data      129
Germ      23
Germ process      31
Gibbs distribution      196 198
Gibbs sampler      227
grain      23 37 40 41
Grain segment      45
Grain, convex      32 35 39
Grain, primary      45
Granulometry for a binary image      66 67
Granulometry for a grey image      99
Greedy algorithm for texture creation      226
Greedy algorithm for texture creation with histogram preservation      220
Haar function      461
Haar wavelet      461 464 482 501
Hammersley — Clifford theorem      211 215
Hilbert curve      45 46
Hilbert scanning      45 48 49
histogram      84 85 522
Histogram of distances      523
Hit-or-miss algorithm      33 34 73—79
Hit-or-miss transform      33 34 73—79
Homogeneity      282
Hurst dimension      117 125 128 129
Ideal feature      319 320
Image basis      550 551 553—561 564—567
Image binarisation by bit-slicing      81 83 85 87 88
Image binarisation by thresholding      81 83 84 87
Image histogram      84 85 234 275
Individual parameters      31 37 38—44 49—51
Inner boundary      69
Interior boundary      32 69
Inverse wavelet transform, continuous      442 456 472
Inverse wavelet transform, discrete      494—499
Inverse Wigner distribution      584 586 588
Isoperimetric problem      383
K-means clustering algorithm      527
Kaiser window      591 592 600
Kullback — Leibler divergence      577
Lacunarity      164 165—167
Lagrange multipliers      383
Language      80
Laplacian pyramid      477
Laws’ masks or filters      539—541 564—567
LBP      573 574 579
Least square error estimation      182
Least square error fitting      162 257—259
Leibniz’s Rule      298
Likelihood      184
Linear process      477
Local binary pattern      573 574 579
Log-likelihood      184
Look-up table      24 27
Lower positive tangent      33
LSE      182
Macro-texture      283 575
Magnetic resonance image      4
Manual segmentation      536
Marker      13 15
Marking probability, ^      5 49
Markov neighbourhood      169 170 173 180 196 197
Markov parameter estimation with LSE      182 189—193
Markov parameter estimation with MLE      182 185—188
Markov parameters      169 171 211 216
Markov random field      165 168 185 201—204
Markov random field, auto-binomial      168 217
Markov random field, auto-normal      180 219
Markov random field, Gaussian      180 219
Markov random field, self-consistent      196
Markov texture features      185
Markovian property      168
Mathematical morphology      12 53 54 68
Mathematical morphology, binary      54
Mathematical morphology, grey scale      90
Maximum entropy clustering      529
Maximum likelihood      182
Maximum Likelihood Estimation      182 185—187
Maximum overlap algorithm      507—511 516 517
Mean free energy      237
Metric      576
Metropolis sampler      227
Mexican hat      450 77
Micro-texture      283
Minkowski subtraction      68
MLE      182
Modulation      518
Mother wavelet      439 442 452
MRF      168
MRI      4
Multiresolution analysis      485 539
Multiresolution analysis, matrices for      514—516
Multiresolution representation      476
Noise suppression      498 499
Normalised filter      410 414 415 418
Normalised histogram      275
Nyquist frequency      592 594 595
Nyquist interval      595
Object, binary, complement of      68
Object, binary, definition of      68
Object, binary, dilation of      68
Object, binary, erosion of      68
Object, binary, reflection of      68
Object, binary, translation of      68
Object, inner boundary of      69
Object, outer boundary of      69
Object, skeleton of      71
Octave      356
Omission error      536 538
one-to-one relationship      23 80
Opening      54 56 57 59 67
Operation, associative      68
Operation, commutative      60 68
Operation, dual      63 69
Optical image      3
Orthogonal functions      335 411
Orthogonal matrix      492
Orthonormal basis      465—472 483
Outer boundary      69
Over-detection error      536 538
Packet wavelet analysis      487 491 504
Parseval’s theorem      301 302 346 384 444
Partition function      198
Pattern recognition      521
Pattern spectrum of a binary image      66 67
Pattern spectrum of a grey image      99 102—104
PCA      521 546 600
Phase spectrum      265
Phase unwrapping      270—274
Placement rules      13 14 15 17 19
Point process      23 29 30 40 45
Poisson probability density function      28 38
Poisson process      28—31 37 38 40 44
Polar coordinates      353—355
Pore segment      45
Power spectrum      132 138—141 146 154—156 262—264
Power spectrum, artifacts of      132 144—146
Power spectrum, fractal approximation of      157—160
Power spectrum, visualisation of      132
Primary grain      45
Primitive pattern      13 53
1 2
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