* Essential reading for engineers and students working in this cutting edge field * Ideal module text and background reference for courses in image processing and computer vision * Companion website includes worksheets, links to free software, Matlab files and new demonstrationsImage processing and computer vision are currently hot topics with undergraduates and professionals alike. Feature Extraction and Image Processing provides an essential guide to the implementation of image processing and computer vision techniques, explaining techniques and fundamentals in a clear and concise manner. Readers can develop working techniques, with usable code provided throughout and working Matlab and Mathcad files on the web.Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals.The new edition includes:* New coverage of curvature in low-level feature extraction (SIFT and saliency) and features (phase congruency); geometric active contours; morphology; camera models* Updated coverage of image smoothing (anistropic diffusion); skeletonization; edge detection; curvature; shape descriptions (moments) * Essential reading for engineers and students working in this cutting edge field* Ideal module text and background reference for courses in image processing and computer vision* Companion website includes worksheets, links to free software, Matlab files and solutions
Author(s): Mark Nixon, Alberto S Aguado
Edition: 2
Publisher: Academic Press
Year: 2008
Language: English
Pages: 424
Tags: Информатика и вычислительная техника;Обработка медиа-данных;Обработка изображений;
Front Cover......Page 1
Feature Extraction and Image Processing......Page 4
Copyright Page......Page 5
Table of Contents......Page 6
Preface......Page 12
1.2 Human and computer vision......Page 18
1.3 The human vision system......Page 20
1.3.1 The eye......Page 21
1.3.2 The neural system......Page 23
1.3.3 Processing......Page 24
1.4 Computer vision systems......Page 26
1.4.1 Cameras......Page 27
1.4.2 Computer interfaces......Page 29
1.4.3 Processing an image......Page 31
1.5 Mathematical systems......Page 32
1.5.2 Hello Mathcad, hello images!......Page 33
1.5.3 Hello Matlab!......Page 38
1.6.1 Journals and magazines......Page 41
1.6.2 Textbooks......Page 42
1.6.3 The web......Page 45
1.8 References......Page 46
2.1 Overview......Page 50
2.2 Image formation......Page 51
2.3 The Fourier transform......Page 54
2.4 The sampling criterion......Page 60
2.5.1 One-dimensional transform......Page 64
2.5.2 Two-dimensional transform......Page 66
2.6.1 Shift invariance......Page 71
2.6.3 Frequency scaling......Page 73
2.6.4 Superposition (linearity)......Page 74
2.7.1 Discrete cosine transform......Page 75
2.7.2 Discrete Hartley transform......Page 76
2.7.3 Introductory wavelets: the Gabor wavelet......Page 78
2.7.4 Other transforms......Page 80
2.8 Applications using frequency domain properties......Page 81
2.9 Further reading......Page 82
2.10 References......Page 83
3.1 Overview......Page 86
3.2 Histograms......Page 87
3.3.1 Basic point operations......Page 88
3.3.2 Histogram normalization......Page 91
3.3.3 Histogram equalization......Page 92
3.3.4 Thresholding......Page 94
3.4.1 Template convolution......Page 98
3.4.2 Averaging operator......Page 101
3.4.3 On different template size......Page 104
3.4.4 Gaussian averaging operator......Page 105
3.5.1 More on averaging......Page 107
3.5.2 Median filter......Page 108
3.5.3 Mode filter......Page 111
3.5.4 Anisotropic diffusion......Page 113
3.5.5 Force field transform......Page 118
3.5.6 Comparison of statistical operators......Page 119
3.6 Mathematical morphology......Page 120
3.6.1 Morphological operators......Page 121
3.6.2 Grey-level morphology......Page 124
3.6.3 Grey-level erosion and dilation......Page 125
3.6.4 Minkowski operators......Page 126
3.7 Further reading......Page 129
3.8 References......Page 130
4.1 Overview......Page 132
4.2.1 Basic operators......Page 134
4.2.2 Analysis of the basic operators......Page 136
4.2.3 Prewitt edge detection operator......Page 138
4.2.4 Sobel edge detection operator......Page 140
4.2.5 Canny edge detection operator......Page 146
4.3.2 Basic operators: the Laplacian......Page 154
4.3.3 Marr–Hildreth operator......Page 156
4.4 Other edge detection operators......Page 161
4.5 Comparison of edge detection operators......Page 162
4.6 Further reading on edge detection......Page 163
4.7 Phase congruency......Page 164
4.8 Localized feature extraction......Page 169
4.8.1.1 Definition of curvature......Page 170
4.8.1.2 Computing differences in edge direction......Page 171
4.8.1.3 Measuring curvature by changes in intensity (differentiation)......Page 173
4.8.1.4 Moravec and Harris detectors......Page 176
4.8.2.1 Scale invariant feature transform......Page 180
4.8.2.2 Saliency......Page 183
4.9 Describing image motion......Page 184
4.9.1 Area-based approach......Page 185
4.9.2 Differential approach......Page 188
4.9.3 Further reading on optical flow......Page 194
4.11 References......Page 195
5.1 Overview......Page 200
5.2 Thresholding and subtraction......Page 201
5.3.1 Definition......Page 203
5.3.2 Fourier transform implementation......Page 210
5.4.1 Overview......Page 213
5.4.2 Lines......Page 214
5.4.3 Hough transform for circles......Page 220
5.4.4 Hough transform for ellipses......Page 224
5.4.5.1 Parameter space reduction for lines......Page 227
5.4.5.2 Parameter space reduction for circles......Page 229
5.4.5.3 Parameter space reduction for ellipses......Page 234
5.5.1 Formal definition of the GHT......Page 238
5.5.2 Polar definition......Page 240
5.5.3 The GHT technique......Page 241
5.5.4 Invariant GHT......Page 245
5.6 Other extensions to the Hough transform......Page 252
5.7 Further reading......Page 253
5.8 References......Page 254
6.1 Overview......Page 258
6.2 Deformable templates......Page 259
6.3.1 Basics......Page 261
6.3.2 The greedy algorithm for snakes......Page 263
6.3.3 Complete (Kass) snake implementation......Page 269
6.3.5 Further snake developments......Page 274
6.3.6 Geometric active contours......Page 278
6.4.1 Distance transforms......Page 283
6.4.2 Symmetry......Page 285
6.5 Flexible shape models: active shape and active appearance......Page 289
6.6 Further reading......Page 292
6.7 References......Page 293
7.1 Overview......Page 298
7.2.1 Boundary and region......Page 299
7.2.2 Chain codes......Page 300
7.2.3 Fourier descriptors......Page 302
7.2.3.1 Basis of Fourier descriptors......Page 303
7.2.3.2 Fourier expansion......Page 304
7.2.3.3 Shift invariance......Page 306
7.2.3.4 Discrete computation......Page 307
7.2.3.5 Cumulative angular function......Page 309
7.2.3.6 Elliptic Fourier descriptors......Page 318
7.2.3.7 Invariance......Page 322
7.3.1 Basic region descriptors......Page 328
7.3.2.1 Basic properties......Page 332
7.3.2.2 Invariant moments......Page 335
7.3.2.3 Zernike moments......Page 337
7.3.2.4 Other moments......Page 341
7.4 Further reading......Page 342
7.5 References......Page 343
8.1 Overview......Page 346
8.2 What is texture?......Page 347
8.3.2 Structural approaches......Page 349
8.3.3 Statistical approaches......Page 352
8.3.4 Combination approaches......Page 354
8.4.1 The k-nearest neighbour rule......Page 356
8.5 Segmentation......Page 360
8.6 Further reading......Page 362
8.7 References......Page 363
9.1 Example Mathcad worksheet for Chapter 3......Page 366
9.2 Example Matlab worksheet for Chapter 4......Page 369
10.2 Perspective camera......Page 372
10.3.1 Homogeneous coordinates and projective geometry......Page 374
10.3.1.2 Ideal points......Page 375
10.3.1.3 Transformations in the projective space......Page 376
10.3.2 Perspective camera model analysis......Page 377
10.3.3 Parameters of the perspective camera model......Page 380
10.4 Affine camera......Page 381
10.4.1 Affine camera model......Page 382
10.4.2 Affine camera model and the perspective projection......Page 383
10.4.3 Parameters of the affine camera model......Page 385
10.5 Weak perspective model......Page 386
10.6 Example of camera models......Page 388
10.7 Discussion......Page 396
10.8 References......Page 397
11.1 The least squares criterion......Page 398
11.2 Curve fitting by least squares......Page 399
12.2 Data......Page 402
12.3 Covariance......Page 403
12.4 Covariance matrix......Page 405
12.5 Data transformation......Page 406
12.6 Inverse transformation......Page 407
12.7 Eigenproblem......Page 408
12.9 PCA method summary......Page 409
12.10 Example......Page 410
12.11 References......Page 415
Index......Page 416
