Advanced color image processing and analysis

Cover \r......Page 1
Title\r......Page 4
Preface 6......Page 6
Contents\r......Page 8
1......Page 10
2 CIECAM02 and Its Recent Developments.      19......Page 28
3 Colour Difference Evaluation      59......Page 68
4 Cross-Media Color Reproduction and Display Characterization      81......Page 90
5 Dihedral Color Filtering      119......Page 128
6 Color Representation and Processes with Clifford Algebra      147......Page 156
Stochastic models, parametric. See Parametric stochastic models Streaming video websites application, 181-182......Page 190
8 Color Image Segmentation      219......Page 228
9 Parametric Stochastic Modeling for Color Image Segmentation and Texture Characterization      279......Page 288
10 Color Invariants for Object Recognition.      327......Page 336
11 Motion Estimation in Colour Image Sequences .      377......Page 386
12 Protection of Colour Images by Selective Encryption      397......Page 406
13 Quality Assessment of Still Images      423......Page 432
14 Image Spectrometers, Color High Fidelity, and Fine-Art Paintings      449......Page 458
15 Application of Spectral Imaging to Electronic Endoscopes      485......Page 494
Index 499......Page 508
Z......Page 0
Liquid crystal tunable filters (LCTF), 458......Page 467
Virtual electrical field (VEF), 252......Page 261
Radial basis function (RBF), 12......Page 21
difference formulas, 63......Page 72
detection, chrominance components Huffman vector, 405......Page 414
Zero run length (ZRL), 401......Page 410
Partial encryption (PE), 402......Page 411
zigzag permutation, 403......Page 412
construction, plaintext cryptographic hashing, 407......Page 416
(MSER) Multiband camera arbitrary illumination, 474......Page 483
CFA color, 475......Page 484
filter-based sequential systems, 473-475......Page 482
472......Page 481
pixels sub-images, 471......Page 480
imaging fine art paintings, 469-479......Page 478
pixel monochrome camera, 470......Page 479
RAM. See Rank agreement measure (RAM) Rank agreement measure (RAM), 269......Page 278
Autoregressive (AR) model L*a*b* color space, 322......Page 331
MGMRF, 288-289......Page 297
mixture and color image segmentation, 289-304......Page 298
linear, 71......Page 80
embedded, 68-69......Page 77
inter and intra observer variability, 74......Page 83
Laplacian-of-Gaussian (LOG), 187......Page 196
memory-matching technique, 31......Page 40
Spectral color space, 8......Page 17
corresponding colours predicted, fifty-two pairs, 33-34......Page 42
CMCCAT2000, 32-34......Page 41
hue (h and H), 28......Page 37
physiological mechanisms, 29-30......Page 38
Hunt effect, 34-35......Page 43
cone types (RGB), 30......Page 39
experimental colour discrimination ellipses, 66......Page 75
CIE TC1-57, 76......Page 85
reference conditions and CMC, 64......Page 73
OSA-UCS and Euclidean, 69......Page 78
DIN99, 67......Page 76
HPE matrix, 47......Page 56
standard colorimetric observers/colour matching functions, 20......Page 29
lightness (J), 26......Page 35
Helson-Judd effect, 36......Page 45
domain, ICC profile connection space, 46-47......Page 55
Window color system (WCS), 48......Page 57
TC8-11, 45......Page 54
viewing conditions adapting field, 25......Page 34
reverse mode, 52-55......Page 61
size effect predictions paint industry and display manufacturers, 40......Page 49
outputs, 43-44......Page 52
HLS and E fi Z 2......Page 11
luminance level., 44......Page 53
original and 24......Page 33
unrelated colours, configuration, 22......Page 31
Tele-spectroradiometer (TSR), 23......Page 32
viewing parameters, 21......Page 30
S-CIELAB, 75-76......Page 84
coordinate a*, 65......Page 74
mobile devices, 419......Page 428
408......Page 417
Fourier transform Clifford colour, spin characters colour spectrum, 175-178......Page 184
quaternion/Clifford algebra Clifford Fourier transforms, 165-166......Page 174
numerical analysis, 162-165......Page 171
definition, 171-172......Page 180
quaternionic Fourier transforms, 161......Page 170
classical one-dimensional formula, 166-167......Page 175
classical digital colour processing images, 160......Page 169
properties, 172-175......Page 181
rotation, 167-168......Page 176
rotations, 169-170......Page 178
Spin characters, 168-169......Page 177
usual transform, 170-171......Page 179
Quaternion definition, 148-149......Page 157
spatial approach colour transform, 154-156......Page 163
definition, 152-154......Page 161
geometric algebra formalism, colour edges, 156-157......Page 165
performance evaluation closed-loop segmentation, 262-263......Page 271
distances and (see Distances) object-recognition systems, 330......Page 339
Color invariants description, 331......Page 340
goal, 328-329......Page 337
colorful banners, 14......Page 23
15......Page 24
16......Page 25
trichomatric imaging, 453......Page 462
high-fidelity capabilities, 454......Page 463
R 3......Page 12
4......Page 13
measurement, physical property, 13......Page 22
textile and paper industry, 10......Page 19
Young-Helmholz theory, 5......Page 14
Refiectance spectrum, 7......Page 16
traditional color, 6......Page 15
Retinex theory, 11......Page 20
Foveon X3, 456-457......Page 465
multi-sensor, 455-456......Page 464
hyperspectral, 457......Page 466
psychophysical, 451......Page 460
digital image, 450......Page 459
image-acquisition system model basic model, 461-462......Page 470
spatial and spectral dimensions, 460......Page 469
Spectral imaging. See also Spectral refiectance BSSDF, 463......Page 472
spectral acquisition and reconstruction classification (see Spectral refiectance) integral equation, 465-466......Page 474
pixel class and singleton potential, 292......Page 301
images, various resolutions and possible segmentation, 222......Page 231
terminal nodes, 254......Page 263
color images, 246......Page 255
structure, 241......Page 250
unsupervised approaches, 245......Page 254
302......Page 311
Segmentation quality metric (SQM), 221......Page 230
291......Page 300
Earth mover’s distance (EMD), 230......Page 239
definition, 220......Page 229
distances and similarity measures, 226-230......Page 235
texture level, regions/zones, 231......Page 240
Ri regions, 223......Page 232
290......Page 299
human vision, 270......Page 279
225......Page 234
pseudo-likelihood, 293......Page 302
JSEG, 301......Page 310
label image, 224......Page 233
segmentation, 300......Page 309
original F metric, 267-268......Page 276
260......Page 269
PSNR. See Peak signal to noise ratio (PSNR) Pyramidal segmentation algorithms, 264......Page 273
Supervised segmentation, 263-264......Page 272
posterior distribution acceptance probability, 299-300......Page 308
class splitting, 297-298......Page 306
Metropolis-Hastings method, 294......Page 303
numerical stability, 298......Page 307
Metropolis-Hastings method, 295......Page 304
description, 82......Page 91
display color (see Display color characterization) intelligent displays, 114......Page 123
media value and point-wise, 113......Page 122
inaccuracy, 104......Page 113
average and maximum error, 112......Page 121
time and measurement, 109......Page 118
forward model, 110......Page 119
Just noticeable difference (JND), 111-112......Page 120
device colorimetric characterization, 84-88......Page 93
first rough/draft model, 85......Page 94
output devices, 87-88......Page 96
matrix, 86......Page 95
SQM. See Segmentation quality metric (SQM) Standard observer, 9......Page 18
Keypoint detection description, 354......Page 363
355......Page 364
Viewpoint invariance, 353......Page 362
356......Page 365
transformation, RGB components, 347......Page 356
distributions, normalizations moments, 346......Page 355
transformation, 345-346......Page 354
matte surface, 348......Page 357
Color ratios) spatial derivatives description, 350-351......Page 359
(LCTF)) Shadow invariance highlight, 352-353......Page 361
quasi-invariance, 351-352......Page 360
Lambertian, 332......Page 341
properties, 333-334......Page 342
sensitivities, 334-335......Page 343
Profile connection space (PCS), 83......Page 92
Color ratios narrowband sensors and illumination Lambertian model, 339-340......Page 348
matte surface, 340-341......Page 349
single pixel Lambertian model, narrowband sensors and blackbody illuminant, 343-344......Page 352
matte surface and ideal white illumination, 342-343......Page 351
neutral interface refiection, balanced sensors,  and ideal white illumination, 341-342......Page 350
image recording, 487......Page 496
color reproduction (see Color reproduction theory) configuration and photograph, 488......Page 497
mean color difference, 490......Page 499
489......Page 498
J-criterion, 234-235......Page 243
powerful tool, manage color, 17......Page 26
Color structure code (CSC) hexagonal hierarchical island structure, 243......Page 252
determination, 244-245......Page 253
Local binary patterns (LBP), 314......Page 323
segmentation, color textures, 315-321......Page 324
312......Page 321
Spectral analysis, IHLS and L*a*b* luminance-chrominance interference color texture, FFT, 309......Page 318
310......Page 319
spatial distribution, 313......Page 322
transform (SIFT) descriptor Similarity measure distance-based normalized, 311......Page 320
vistex and photoshop databases, 317......Page 326
LPE, 316......Page 325
mean percentages, pixel classification errors, 321......Page 330
Wishart distribution average percentage error, 319......Page 328
(PLVC) models POCS. See Projection onto convex sets (POCS) Potts model, 318......Page 327
320......Page 329
chroma (C) and saturation (s), 27......Page 36
definition, 61......Page 70
Parametric effects, 60......Page 69
natural logarithms and worse agreement, 72......Page 81
multidimensional scaling and PF/3, 73......Page 82
visual vs. instrumental, 62......Page 71
transformation, tristimulus values, 70......Page 79
confidentiality, 399-400......Page 408
face detection, 417......Page 426
DCT. See Discrete cosine transform (DCT) DFT. See Discrete Fourier transform (DFT) Digital rights management (DRM), 398......Page 407
Quality factor (QF), 410......Page 419
412......Page 421
411......Page 420
413......Page 422
motion estimation and tracking, 420......Page 429
human skin, 404......Page 413
low and upper scale, 250......Page 259
HVS, 437......Page 446
Neugebauer primaries (NP), 88......Page 97
three-parameter extreme-value distribution model, 144......Page 153
digital color images, 120......Page 129
RGB vectors, 121......Page 130
NEDI algorithm, 192......Page 201
Steerable filters, 128......Page 137
125......Page 134
Riesz representation theorem, 127......Page 136
126......Page 135
packages, 139-143......Page 148
collections, 138-139......Page 147
EVT and histogram, andy warhol- claude monet set, 140......Page 149
143......Page 152
SVM-ranked images resulting, 141......Page 150
Intertwining operator, 129......Page 138
Extreme value theory (EVT) accumulator and stochastic processes, 131......Page 140
black box, 130-131......Page 139
distribution families, 132......Page 141
137......Page 146
vector components and polar coordinates, 136......Page 145
Fast Fourier transform (FFT), 135......Page 144
Huffman coding block, 400......Page 409
pixel interval, 459......Page 468
Wiener estimation (see Wiener estimation) Spectrometer colorimeter, 91-92......Page 100
Thin plate splines (TPS), 92......Page 101
98......Page 107
LC technology and gamma, 95......Page 104
definition, 102......Page 111
PLCC, 94......Page 103
Minkowski distance, 229......Page 238
Hamming distance, 228......Page 237
color-specific, 227-228......Page 236
multichannel/vectorial, 286-289......Page 295
trichromatic theory, 486......Page 495
492......Page 501
observation wavelengths and rapid switching, 495......Page 504
496......Page 505
sensory and perceptive processes, 425......Page 434
multiple dimensions, chi-square, 283......Page 292
image type and model distribution, 133......Page 142
original image, edge filter result, and tails (maxima), 134......Page 143
histogram, 232......Page 241
run-length matrix, 238......Page 247
494......Page 503
493......Page 502
433......Page 442
RGB color space, 239......Page 248
pseudo-images, 240......Page 249
WCS. See Window color system (WCS) Weber’s law, 97......Page 106
PLCC* and S-curve, 101......Page 110
spatial and categorization, 89......Page 98
optimal, definition, 90......Page 99
MRF and GMRF, 284-286......Page 293
variance matrix, 285......Page 294
traditional methods, 380......Page 389
Mincut, 255......Page 264
fi value, 256-257......Page 265
257......Page 266
sink node, 258......Page 267
pixels, high-resolution image, 182-183......Page 191
185......Page 194
Stevens effect, 35......Page 44
proposed SE method, 406......Page 415
DCT coefficients, ROI, 409......Page 418
key-region detection hierarchical segmentation, 361......Page 370
476......Page 485
ideal white, 336......Page 345
Planckian blackbody, 335......Page 344
linear and affine transformation, 337-338......Page 346
monotonically increasing functions, 338......Page 347
Mean squared error (MSE) low-complexity metrics, 445......Page 454
Normalized cross correlation (NCC), 434......Page 443
signal processing, 431......Page 440
RMSE. See Root mean squared error (RMSE) Root mean squared error (RMSE) accurate prediction capability, 440......Page 449
VEF. See Virtual electrical field (VEF) Video quality experts group (VQEG) correlation analysis, 441......Page 450
outliers ratio, 443......Page 452
Spearman rank order correlation, 442-443......Page 451
image quality assessment, 444......Page 453
psychophysical tests, 429......Page 438
428......Page 437
viewing conditions, 427......Page 436
International telecommunication union (ITU) digital compression subjective testing, 426......Page 435
types, measurement, 424......Page 433
PSNR use, metrics and calculation, 211......Page 220
MAP approach, 202......Page 211
test material, 208......Page 217
procedure, 210-211......Page 219
Original Lena and Lighthouse image, 188......Page 197
initial estimate image, 197......Page 206
conserves textures and edges, 201......Page 210
framework, 186-187......Page 195
use, 183......Page 192
variational interpolation approach, 200......Page 209
geometric fiow, 198......Page 207
structure tensor, 199......Page 208
imaging process, 195......Page 204
194......Page 203
operators, 184......Page 193
wavelet transform, 189-191......Page 198
super-resolution process, 196......Page 205
uniform mapping, CMY and nonuniform mapping, CIELAB space, 106......Page 115
107......Page 116
105......Page 114
247......Page 256
watershed process, 249......Page 258
merging threshold, 248......Page 257
Hessian-based detector, 358-359......Page 367
Moravec detector, 357......Page 366
Harris-Laplace and Hessian-Laplace, 359-360......Page 368
Maximally stable extremal region (MSER), 360-361......Page 369
learning, detection attention, 362......Page 371
object-background classification approach, 363......Page 372
gray-level images, 281......Page 290
Markov random fields (MRF) model (cont.) learning-based methods, 207......Page 216
database, facial expressions, 205......Page 214
MAP high-resolution image, 206......Page 215
POCS super-resolution reconstruction approach, 204......Page 213
Multichannel complex linear prediction models, 287......Page 296
PSD estimation methods, 304-306......Page 313
upper limits, interpretation, 432......Page 441
HVS, 436......Page 445
limb, 435......Page 444
ML approach, 203......Page 212
subjective scores, 212......Page 221
214......Page 223
Pearson correlation coefficient (PCC), 216......Page 225
215......Page 224
213......Page 222
error measures, 430......Page 439
Motion estimation data-fusion algorithm, 394......Page 403
least squares and pseudo-inverse calculation, 382......Page 391
results, 383-386......Page 392
psychological and biological evidence, 378......Page 387
393......Page 402
Optical fiow equation (OFE), 379......Page 388
sparse optical fiow methods block-based, colour wavelet pyramids, 389-393......Page 398
large displacement estimation, 386-389......Page 395
UCS. See Uniform Chromaticity Scale (UCS) Uniform Chromaticity Scale (UCS), 381......Page 390
Probability density function (PDF), 282......Page 291
Multivariate Gaussian mixture models (MGMM) approximation, color distribution, 303......Page 312
two channel complex sinusoidal images, 307......Page 316
pixel, 280......Page 289
on wavelet pyramid, 392......Page 401
Teo and Heeger model, 439......Page 448
perceptual decomposition, 438......Page 447
spatial transformations and orbit D4x, 122......Page 131
X , Y and Z , LCD display function, 96......Page 105
Piecewise linear-assuming chromaticity constancy (PLCC) models, 93......Page 102
tristimulus values, X , Y , and Z , 103......Page 112
RGB, 306......Page 315
luminance channel, 305-306......Page 314
242......Page 251
quality metric, properties, 268......Page 277
266......Page 275
model-based recognition and graph matching, 265......Page 274
quaternionic filtering, 150-152......Page 159
transformations, 149-150......Page 158
415......Page 424
416......Page 425
sequence, 414......Page 423
diffeomorphism fifififi dimension matching, 296......Page 305
description, 364......Page 373
types, histograms, 365......Page 374
object tracking and auto-correlograms, 367......Page 376
versions, 368......Page 377
sequential combination, 366-367......Page 375
YCrCb color space, 369......Page 378
370......Page 379
Selective encryption (SE). See Colour image protection, SE Semantic gap, 261......Page 270
Spatial filtering, Clifford algebra AG filtering, 159......Page 168
Sangwine’s method, 157-158......Page 166
scalar and bivectorial parts, 158......Page 167
zero mean value and SNR, 308......Page 317
Kubelka-Munk model, 464-465......Page 473
underdrawings, 477-478......Page 486
virtual restoration, 478-479......Page 487
spectral radiance, 491......Page 500
reconstruction direct, 466-467......Page 475
indirect, 467-468......Page 476
interpolation, 468......Page 477
goniospectrometers, 462......Page 471
overlaid RGB cooccurrence matrices, 236......Page 245
J-images, 235-236......Page 244
237......Page 246
4......Page 132
tensor, 124-125......Page 133
Total difference models, 77......Page 86
UCS, 37......Page 46
38......Page 47
39......Page 48