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Petrou M., Bosdogianni P. — Image processing: the fundamentals
Petrou M., Bosdogianni P. — Image processing: the fundamentals

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Название: Image processing: the fundamentals

Авторы: Petrou M., Bosdogianni P.

Аннотация:

Provides a step-by-step guide to the basic principles underlying all image processing tasks. Features numerous worked examples, guiding the reader through the intricacies of reaching solutions and explains the concepts introduced using small sized images that the reader can manipulate without the use of computers. DLC: Image processing — Digital techniques.


Язык: en

Рубрика: Computer science/Обработка изображений/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1999

Количество страниц: 327

Добавлена в каталог: 20.11.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
Additive noise      144
Antisymmetric image features      309
Approximation of an image by SVD      34 35
Approximation of an image using its K-L transform      110 115
Approximation theory for filter design      171—191
Autocorrelation function of a random field      94 103
Autocorrelation matrix of an image (how to compute)      98—102 105—106
Autocovariance of a random field      94
Automatic vision      18
Bandpass filter      160
Bands of an image      1 138
Barrel distortion      194
Basis images for the Fourier transform      68 69
Basis images for the Haar transform      52
Basis images for the Hadamard transform      58
Basis images for the Walsh transform      58
Basis images from the K-L transform      111
Bilinear interpolation      195
Bits needed for an image      2
Block circulant matrix      232 239—240
Brightness of an image pixel      2
Canny's criteria for an optimal edge filter      306—309 318—322
Characteristic equation of a matrix      31
Chebyshev norm      171 172
Checkerboard effect      2
Circulant matrix      233
Clustering      288
Complete set of functions      45
Conditional expectation      217
Constraint matrix inversion      251
Contrast enhancement of a multispectral image      135
Contrast manipulation      132
Convolution      7 72—77 157
Convolution theorem (assumptions of)      75
Covariance of two random variables      92
Cross correlation of two random fields      95
Cross covariance of two random fields      95
Cross spectral density of two random fields      218
Delta function      8
DFT      see "Discrete Fourier transform"
Direct component of an image      82
Discrete cosine transform      86
Discrete Fourier Transform      63—86
Discrete Fourier transform (display of)      79
Discrete Fourier transform and image restoration      210—215 228—230
Discrete Fourier transform of a rotated image      79
Discrete Fourier transform of a scaled image      83
Discrete Fourier transform of a shifted image      81
Distribution function of a random variable      90
Distribution function of many random variables      92
Dual grid      291
Edge detection      265 289—322
Edge map      303
Edge pixels      291
Edgels      291
Eigenimages from SVD      37
Eigenimages from the K-L transform      121
Eigenvalues of a circulant matrix      233
Eigenvalues of a matrix      30 140 234
Eigenvectors of a circulant matrix      233
Eigenvectors of a matrix      30 31 140 234
Ergodic random field      96 97
Ergodic random field with respect to the autocorrelation function      97
Ergodic random field with respect to the mean      97
Ergodicity      102—104 227
Error in approximating an image by its K-L transform      115
Error in approximating an image by its SVD      35
Even symmetrical cosine transform      86
Expansion of an image in terms of eigenimages      34
Expansion of an image in terms of Fourier basis functions      68
Expansion of an image in terms of Haar basis functions      51—52 58—60
Expansion of an image in terms of vector outer products      21—22
Expansion of an image in terms of Walsh/Hadamard matrices      57—58 60—61
Expansion of an image using its K-L transform      110
Expected value of a random variable      92
False contouring      2
Fast Fourier Transform      84
Feature map      315—316
features      288
FFT      see "Fast Fourier transform"
Filter      155
Fourier transform      45 63 72
Frequency convolution theorem      75
Frequency sampling      172 182—191
Fresnel integrals      202—204
Gaussian noise      144
Geometric progression      64
Geometric restoration      193—198
Grey level      1
Grey level interpolation      195
Haar functions      46
Haar transforms      47 62
Haar wavelet      62
Hadamard matrices      57
Hadamard transforms      57
Highpass filter      148 160
Histogram equalization      127
Histogram hyperbolization      129
Histogram modification with random additions      127 130
Histogram of an image      125
Histogram of an image under variable illumination      283—285
Homogeneous random field      96
Homomorphic filter      149—150
Hotelling transform      89
Hysteresis thresholding      266
Ideal highpass filter      148 160
Ideal lowpass filter      148 157—161
Image      1
Image as a linear superposition of point sources      9
Image as a random field      89—121
Image classification      266
Image compression      18
Image compression using K-L      102
Image compression using SVD      24
Image enhancement      18 125—153
Image geometric restoration      193—198
Image labelling      266
Image registration      193
image resolution      2
Image restoration      18 193—263
Image restoration by inverse filtering      209—217
Image restoration by matrix inversion      230—262
Image restoration by Wiener filtering      218—230
Image segmentation      265
Image sharpening      148
Image smoothing      147
Image thresholding      255—286
Image thresholding under variable illumination      285
Impulse noise      144
Independent random variables      92
Inhomogeneous contrast      131
Joint distribution function of many random variables      92
Joint probability density function of many random variables      92
K-L      see "Karhunen — Loeve"
Karhunen — Loeve expansion of an image      110
Karhunen — Loeve transform      89
Karhunen — Loeve transform of a multispectral image      136
Kronecker ordering of Walsh functions      57
Kronecker product of matrices      14 237
La Vallee Poussin theorem      180 181
Lagrange multipliers      251—258
Laplacian      243
Least square error approximation of an image      37
Least square error solution for image restoration      218
Leibnitz rule for differentiating an integral with respect to a parameter      268
Lexicographic ordering of Walsh functions      57
Limiting set of equations      180
Line detection      309—310
Linear operator      6
Linear programming      172 174
Local contrast enhancement      131
Low pass filter      148 157 161
Lowpass filtering      147
Matrix diagonalization      24—35
Maximizing algorithms for filter design      180
Mean square error for K-L transform      118
Mean value of a random variable      92
Median Filter      146
Mini-max algorithms for filter design      180
Minimizing algorithms for filter design      180
Minimum error threshold      268—278
Minimum mean square error approximation of an image      118 124
Minimum square error approximation of an image      36
Motion blurring      200—204 210—217 228—230 259—262
Multiband image      135
Multiplicative noise      144 149 283
Multispectral image      135
Natural order of Walsh functions      47
Nearest neighbour interpolation      195
Noise      144
Noise convolved with a filter      316—317
Noise in image restoration      210
Non-maxima suppression      303
Non-recursive filter      161
Norm of a matrix      35
Operator      6
Optimal threshold      268—278
Orthogonal matrix      24
Orthogonal random variables      92
Orthogonal set of functions      45
Orthonormal set of functions      45
Orthonormal vectors      24
Otsu's thresholding method      278—282
Outer product of vectors      21
Partition of a matrix      10
Pattern recognition      288
Pel      1
pincushion distortion      194
Pixel      1
Point source      7—9
Point spread function of a linear degradation process      198—209
Point spread function of an operator      6—7
Principal component analysis of a multispectral image      136—144
Probability density function of a random variable      91 92
Probability density function of many random variables      92
Probability of an event      90
Properties of a discrete Fourier transform      79—84
Quadtree      289
Ramp edges      309
Random field      90 93
Random variable      90
Rank order filtering      146
Rectangle function      7
Recursive filter      161
Region growing      288
Resolution      2
Restoration by matrix inversion      230—262
Restoration by Wiener filtering      218—230
Restoration of motion blurring      210—217 228—230 259—262
Rice's formula for filtered noise      321
Robinson operators      208
Salt and pepper noise      144
Scaling function      62
Seed pixels for region growing      288
Separability assumption      14—15
Separable masks      306
Separable point spread function      7
Separable transform      15
Sequency order of Walsh functions      47
Sharpening      148
Shift invariant point spread function      7
Shifting property of the delta function      9
Simulated annealing      217
Singular value decomposition of a matrix      24
Singular value decomposition of an image      34
Smoothing      147
Sobel masks      296—299 306
Spatial autocorrelation matrix of an image      105
Spatial statistics of a random field      97
Spectral bands      1 138
Spectral density of a random field      223
Split and merge algorithms      288
Stability condition in filter design      171
Stacking operator      10
Standard deviation of a random variable      92
Successive doubling algorithm      84
SVD      see "Singular value decomposition"
System function      155
Textured regions      288
Thresholding      255
tie points      195
Time convolution theorem      75
Trace of a matrix      36
Transfer function from a bright edge      206—209
Transfer function from a bright line      205—206
Transfer function from an astronomical image      204
Transfer function of a degradation process      199—209
Transfer function of motion blurring      200—204 210—217
Uncorrelated data      144
Uncorrelated random fields      95
Uncorrelated random variables      92
Unit sample response of a filter      155
Unitary matrix      23—24
Unitary transform      23
Variable illumination      149 283—286
Variance of a random variable      92
Vector outer product      21
Vector outer product (expansion of an image in terms of)      21—22
Walsh functions      46 47
Walsh transforms      47 62
Wavelets      62
White noise      220
Wiener filter      218—230 254
Wiener — Khinchine theorem      223
Windowing      172 173 210
z-transform      161—171
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