Digital image processing: an algorithmic introduction using Java
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
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London
Springer
[2016]
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| Ausgabe: | Second edition |
| Schriftenreihe: | Texts in computer science
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XXIII, 809 Seiten Illustrationen (teilweise farbig) |
| ISBN: | 9781447166832 |
| ISSN: | 1868-0941 |
Internformat
MARC
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| 100 | 1 | |a Burger, Wilhelm |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Digital image processing |b an algorithmic introduction using Java |c Wilhelm Burger, Mark J. Burge |
| 250 | |a Second edition | ||
| 264 | 1 | |a London |b Springer |c [2016] | |
| 264 | 4 | |c © 2016 | |
| 300 | |a XXIII, 809 Seiten |b Illustrationen (teilweise farbig) | ||
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| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Texts in computer science |x 1868-0941 | |
| 650 | 4 | |a Computer science | |
| 650 | 4 | |a Computational intelligence | |
| 650 | 4 | |a Image Processing and Computer Vision | |
| 650 | 4 | |a Signal, Image and Speech Processing | |
| 650 | 4 | |a Informatik | |
| 650 | 0 | 7 | |a Informatik |0 (DE-588)4026894-9 |2 gnd |9 rswk-swf |
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| 700 | 1 | |a Burge, Mark James |e Verfasser |0 (DE-588)132219336 |4 aut | |
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| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028935397&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
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Datensatz im Suchindex
| _version_ | 1804176164933074944 |
|---|---|
| adam_text | Contents
1 Digital Images....................................... 1
1.1 Programming with Images............................ 2
1.2 Image Analysis and Computer Vision................. 2
1.3 Types of Digital Images ........................... 4
1.4 Image Acquisition.................................. 4
1.4.1 The Pinhole Camera Model..................... 4
1.4.2 The “Thin” Lens ............................. 6
1.4.3 Going Digital ............................... 7
1.4.4 Image Size and Resolution.................... 8
1.4.5 Image Coordinate System...................... 9
1.4.6 Pixel Values................................. 9
1.5 Image File Formats ............................... 11
1.5.1 Raster versus Vector Data................... 12
1.5.2 Tagged Image File Format (TIFF)............. 12
1.5.3 Graphics Interchange Format (GIF)........... 13
1.5.4 Portable Network Graphics (PNG)............. 14
1.5.5 JPEG........................................ 14
1.5.6 Windows Bitmap (BMP) ....................... 18
1.5.7 Portable Bitmap Format (PBM)................ 18
1.5.8 Additional File Formats..................... 18
1.5.9 Bits and Bytes.............................. 19
1.6 Exercises ...................................... 21 2
2 Imaged .............................................. 23
2.1 Software for Digital Imaging ..................... 24
2.2 ImageJ Overview................................... 24
2.2.1 Key Features............................... 25
2.2.2 Interactive Tools.......................... 26
2.2.3 ImageJ Plugins............................. 26
2.2.4 A First Example: Inverting an Image........ 28
2.2.5 Plugin My_Inverter_A (using PluglnFilter) 28
2.2.6 Plugin My_Inverter_B (using Plugin) ....... 29
2.2.7 When to use Plugin or PluglnFilter?...... 30
2.2.8 Executing Imaged “Commands”................ 32
2.3 Additional Information on Imaged and Java......... 34
2.3.1 Resources for Imaged....................... 34
2.3.2 Programming wdth Java....................... 34
2.4 Exercises ........................................ 34
XI
XII
Contents ^ Histograms and Image Statistics........................... 37
3.1 What is a Histogram?................................ 38
3.2 Interpreting Histograms ............................ 39
3.2.1 Image Acquisition............................ 39
3.2.2 Image Defects................................ 41
3.3 Calculating Histograms.............................. 43
3.4 Histograms of Images with More than 8 Bits...... 45
3.4.1 Binning ..................................... 45
3.4.2 Example...................................... 45
3.4.3 Implementation............................... 46
3.5 Histograms of Color Images.......................... 46
3.5.1 Intensity Histograms......................... 47
3.5.2 Individual Color Channel Histograms.......... 47
3.5.3 Combined Color Histograms ................... 48
3.6 The Cumulative Histogram ........................... 49
3.7 Statistical Information from the Histogram.......... 49
3.7.1 Mean and Variance............................ 50
3.7.2 Median....................................... 51
3.8 Block Statistics.................................... 51
3.8.1 Integral Images.............................. 51
3.8.2 Mean Intensity............................... 53
3.8.3 Variance..................................... 53
3.8.4 Practical Calculation of Integral Images .... 53
3.9 Exercises .......................................... 54
4 Point Operations..................................... 57
4.1 Modifying Image Intensity........................... 58
4.1.1 Contrast and Brightness...................... 58
4.1.2 Limiting Values by Clamping.................. 58
4.1.3 Inver t ing Images........................... 59
4.1.4 Threshold Operation.......................... 59
4.2 Point Operations and Histograms..................... 59
4.3 Automatic Contrast Adjustment....................... 61
4.4 Modified Auto-Contrast Operation.................... 62
4.5 Histogram Equalization.............................. 63
4.6 Histogram Specification............................. 66
4.6.1 Frequencies and Probabilities................ 67
4.6.2 Principle of Histogram Specification......... 67
4.6.3 Adjusting to a Piecewise Linear Distribution 68
4.6.4 Adjusting to a Given Histogram (Histogram
Matching) ................................... 70
4.6.5 Examples..................................... 71
4.7 Gamma Correction.................................... 74
4.7.1 Why Gamma?................................... 75
4.7.2 Mathematical Definition...................... 77
4.7.3 Real Gamma Values ........................... 77
4.7.4 Applications of Gamma Correction............. 78
4.7.5 Implementation............................... 79
4.7.6 Modified Gamma Correction.................... 80
4.8 Point Operations in Imaged.......................... 82
4.8.1 Point Operations with Lookup Tables...... 82
4.8.2 Arithmetic Operations ....................... 83
4.8.3 Point Operations Involving Multiple Images . 83 Contents
4.8.4 Methods for Point Operations on Two Images 84
4.8.5 ImageJ Plugins Involving Multiple Images . . 85
4.9 Exercises .......................................... 86
5 Filters................................................ 89
5.1 What is a Filter? .................................. 89
5.2 Linear Filters ..................................... 91
5.2.1 The Filter Kernel............................ 91
5.2.2 Applying the Filter ......................... 91
5.2.3 Implementing the Filter Operation............ 93
5.2.4 Filter Plugin Examples....................... 93
5.2.5 Integer Coefficients......................... 95
5.2.6 Filters of Arbitrary Size.................... 96
5.2.7 Types of Linear Filters...................... 97
5.3 Formal Properties of Linear Filters................. 99
5.3.1 Linear Convolution ......................... 100
5.3.2 Formal Properties of Linear Convolution .... 101
5.3.3 Separability of Linear Filters.............. 102
5.3.4 Impulse Response of a Filter................ 104
5.4 Nonlinear Filters.................................. 105
5.4.1 Minimum and Maximum Filters................. 105
5.4.2 Median Filter............................... 107
5.4.3 Weighted Median Filter ..................... 109
5.4.4 Other Nonlinear Filters..................... Ill
5.5 Implementing Filters............................... 112
5.5.1 Efficiency of Filter Programs............... 112
5.5.2 Handling Image Borders...................... 113
5.5.3 Debugging Filter Programs .................. 114
5.6 Filter Operations in Imaged........................ 115
5.6.1 Linear Filters ............................. 115
5.6.2 Gaussian Filters............................ 115
5.6.3 Nonlinear Filters .......................... 116
5.7 Exercises ......................................... 116
6 Edges arid Contours.................................... 121
6.1 What Makes an Edge?................................ 121
6.2 Gradient-Based Edge Detection...................... 122
6.2.1 Partial Derivatives and the Gradient........ 123
6.2.2 Derivative Filters.......................... 123
6.3 Simple Edge Operators.............................. 124
6.3.1 Prewitt and Sobel Operators................. 125
6.3.2 Roberts Operator............................ 127
6.3.3 Compass Operators........................... 128
6.3.4 Edge Operators in Imaged.................... 130
6.4 Other Edge Operators.............................. 130
6.4.1 Edge Detection Based on Second Derivatives 130
6.4.2 Edges at Different Scales ................. 1.30
6.4.3 From Edges to Contours...................... 131
6.5 Canny Edge Operator.............................. 132
6.5.1 Pre-processing.............................. 134
6.5.2 Edge localization........................... 134
XIII
Contents
XIV
6.5.3 Edge tracing and hysteresis thresholding .... 135
6.5.4 Additional Information....................... 137
6.5.5 Implementation............................... 138
6.6 Edge Sharpening.................................... 139
6.6.1 Edge Sharpening with the Laplacian Filter . . 139
6.6.2 Unsharp Masking.............................. 142
6.7 Exercises ......................................... 146
7 Corner Detection..................................... 147
7.1 Points of Interest................................. 147
7.2 Harris Corner Detector............................. 148
7.2.1 Local Structure Matrix....................... 148
7.2.2 Corner Response Function (CRF)............... 149
7.2.3 Determining Corner Points.................... 149
7.2.4 Examples..................................... 150
7.3 Implementation..................................... 152
7.3.1 Step 1: Calculating the Corner Response
Function..................................... 153
7.3.2 Step 2: Selecting “Good” Corner Points .... 155
7.3.3 Step 3: Cleaning up.......................... 156
7.3.4 Summary...................................... 157
7.4 Exercises ......................................... 158
8 Finding Simple Curves: The Hough Transform . . . 161
8.1 Salient Image Structures........................... 161
8.2 The Hough Transform................................ 162
8.2.1 Parameter Space.............................. 163
8.2.2 Accumulator Map.............................. 164
8.2.3 A Better Line Representation................. 165
8.3 Hough Algorithm.................................... 167
8.3.1 Processing the Accumulator Array............. 168
8.3.2 Hough Transform Extensions................... 170
8.4 Java Implementation................................ 173
8.5 Hough Transform for Circles and Ellipses........... 176
8.5.1 Circles and Arcs............................. 176
8.5.2 Ellipses..................................... 177
8.6 Exercises ......................................... 179
9 Morphological Filters.................................... 181
9.1 Shrink and Let Grow................................ 182
9.1.1 Neighborhood of Pixels....................... 183
9.2 Basic Morphological Operations .................... 183
9.2.1 The Structuring Element ..................... 183
9.2.2 Point Sets .................................. 184
9.2.3 Dilation..................................... 185
9.2.4 Erosion...................................... 186
9.2.5 Formal Properties of Dilation and Erosion . . 186
9.2.6 Designing Morphological Filters ............. 188
9.2.7 Application Example: Outline................. 189
9.3 Composite Morphological Operations................. 192
9.3.1 Opening...................................... 192
9.3.2 Closing...................................... 192
9.3.3 Properties of Opening and Closing.......... 193
9.4 Thinning (Skeletonization)....................... 194
9.4.1 Thinning Algorithm by Zhang and Siren. .... 194
9.4.2 Fast Thinning Algorithm.................... 195
9.4.3 Java Implementation........................ 198
9.4.4 Built-in Morphological Operations in ImageJ 201
9.5 Grayscale Morphology............................. 202
9.5.1 Structuring Elements....................... 202
9.5.2 Dilation and Erosion....................... 203
9.5.3 Grayscale Opening and Closing.............. 203
9.6 Exercises........................................ 205
10 Regions in Binary Images.............................. 209
10.1 Finding Connected Image Regions ................. 210
10.1.1 Region Labeling by .Flood Idling.......... 210
10.1.2 Sequential Region Labeling ............... 213
10.1.3 Region Labeling—Summary................... 219
10.2 Region Contours.................................. 219
10.2.1 External and Internal Contours............ 219
10.2.2 Combining Region Labeling and Contour
Finding.................................... 220
10.2.3 Java Implementation....................... 222
10.3 Representing Image Regions....................... 225
10.3.1 Matrix Representation..................... 225
10.3.2 Run Length Encoding....................... 225
10.3.3 Chain Codes............................... 226
10.4 Properties of Binary Regions..................... 229
10.4.1 Shape Features............................ 229
10.4.2 Geometric Features........................ 230
10.5 Statistical Shape Properties..................... 232
10.5.1 Centroid.................................. 233
10.5.2 Moments................................... 233
10.5.3 Central Moments........................... 234
10.5.4 Normalized Central Moments ............... 234
10.5.5 Java Implementation....................... 234
10.6 Moment-Based Geometric Properties................ 235
10.6.1 Orientation............................... 235
10.6.2 Eccentricity.............................. 237
10.6.3 Bounding Box Aligned to the Major Axis ... 239
10.6.4 Invariant Region Moments.................. 241
10.7 Projections...................................... 244
10.8 Topological Region Properties.................... 244
10.9 Java Implementation.............................. 246
10.10 Exercises ...................................... 246
11 Automatic Thresholding.............................. 253
11.1 Global Histogram-Based Thresholding.............. 253
11.1.1 Image Statistics from the Histogram....... 255
11.1.2 Simple Threshold Selection................ 256
11.1.3 Iterative Threshold Selection (Isodata
Algorithm)................................. 258
11.1.4 Otsu’s Method ............................ 260
Contents
XV
Contents 11.1.5 Maximum Entropy Thresholding.............. 263
11.1.6 Minimum Error Thresholding ............... 266
11.2 Local Adaptive Thresholding...................... 273
11.2.1 Bernserds Method.......................... 274
11.2.2 Niblack’s Method.......................... 275
11.3 Java Implementation.............................. 284
11.3.1 Global Thresholding Methods............... 285
11.3.2 Adaptive Thresholding..................... 287
11.4 Summary and Further Reading...................... 288
11.5 Exercises ....................................... 289
12 Color Images.......................................... 291
12.1 RGB Color Images................................. 291
12.1.1 Structure of Color Images................. 292
12.1.2 Color Images in ImageJ ................... 296
12.2 Color Spaces and Color Conversion................ 303
12.2.1 Conversion to Grayscale................... 304
12.2.2 Desaturating RGB Color Images............. 306
12.2.3 HSV/HSB and HLS Color Spaces.............. 306
12.2.4 TV Component Color Spaces—YUV, YIQ,
and YCbCr ................................. 317
12.2.5 Color Spaces for Printing-—-CMY and CMYK 320
12.3 Statistics of Color Images....................... 323
12.3.1 How Many Different Colors are in an Image? 323
12.3.2 Color Histograms.......................... 324
12.4 Exercises ....................................... 325
13 Color Quantization.................................... 329
13.1 Scalar Color Quantization........................ 329
13.2 Vector Quantization.............................. 331
13.2.1 Populosity Algorithm ..................... 331
13.2.2 Median-Cut Algorithm...................... 332
13.2.3 Octree Algorithm.......................... 333
13.2.4 Other Methods for Vector Quantization .... 336
13.2.5 Java Implementation....................... 337
13.3 Exercises ....................................... 337
14 Colorimetric Color Spaces 341
14.1 CIE Color Spaces................................. 341
14.1.1 CIE XYZ Color Space....................... 342
14.1.2 CIE x,y Chromaticity...................... 342
14.1.3 Standard Illuminants...................... 344
14.1.4 Gamut..................................... 345
14.1.5 Variants of the CIE Color Space........... 345
14.2 CIELAB........................................... 346
14.2.1 CIEXYZ— CIELAB Conversion................. 346
14.2.2 CIELAB— CIEXYZ Conversion.............. 347
14.3 CIELUV........................................... 348
14.3.1 CIEXYZ—iCIELUV Conversion................. 348
14.3.2 CIELUV —»CIEXYZ Conversion ............... 350
14.3.3 Measuring Color Differences............... 350
14.4 Standard RGB (sRGB).............................. 350
XVI
14.4.1 Linear vs. Nonlinear Color Components .... 351
14.4.2 CIBXYZ—rsRGB Conversion...................... 352
14.4.3 sRGB— CIEXYZ Conversion...................... 353
14.4.4 Calculations with Nonlinear sRGB Values. . . 353
14.5 Adobe RGB........................................... 354
14.6 Chromatic Adaptation................................ 355
14.6.1 XYZ Scaling.................................. 355
14.6.2 Bradford Adaptation.......................... 356
14.7 Colorimetric Support in Java........................ 358
14.7.1 Profile Connection Space (PCS) .............. 358
14.7.2 Color-Related Java Classes................... 360
14.7.3 Implementation of the CIELAB Color Space
(Example)..................................... 361
14.7.4 ICC Profiles................................. 362
14.8 Exercises .......................................... 365
15 Filters for Color Images ............................ 367
15.1 Linear Filters ..................................... 367
15.1.1 Monochromatic Application of Linear Filters 368
15.1.2 Color Space Considerations................... 370
15.1.3 Linear Filtering with Circular Values........ 374
15.2 Nonlinear Color Filters............................. 378
15.2.1 Scalar Median Filter......................... 378
15.2.2 Vector Median Filter......................... 378
15.2.3 Sharpening Vector Median Filter ............. 382
15.3 Java implementation................................. 385
15.4 Further Reading..................................... 387
15.5 Exercises .......................................... 388
16 Edge Detection in Color Images....................... 391
16.1 Monochromatic Techniques ........................... 392
16.2 Edges in Vector-Valued Images....................... 395
16.2.1 Multi-Dimensional Gradients.................. 397
16.2.2 The Jacobian Matrix.......................... 397
16.2.3 Squared Local Contrast....................... 398
16.2.4 Color Edge Magnitude ........................ 399
16.2.5 Color Edge Orientation....................... 401
16.2.6 Grayscale Gradients Revisited................ 401
16.3 Canny Edge Detector for Color Images................ 404
16.4 Other Color Edge Operators ......................... 406
16.5 Java Implementation................................. 410
17 Edge-Preserving Smoothing Filters.................... 413
17.1 Kuwahara-Type Filters............................... 414
17.1.1 Application to Color Images.................. 416
17.2 Bilateral Filter ................................... 420
17.2.1 Domain Filter................................ 420
17.2.2 Range Filter ................................ 421
17.2.3 Bilateral Filter—-General Idea............... 421
17.2.4 Bilateral Filter with Gaussian Kernels.. 423
17.2.5 Application to Color Images.................. 424
17.2.6 Efficient Implementation by .r/y Separation . 428
Contents
XVII
Contents 17.2.7 Further Reading........................... 432
17.3 Anisotropic Diffusion Filters.................... 433
17.3.1 Homogeneous Diffusion and the Heat
Equation ................................. 434
17.3.2 Perona-Malik Filter....................... 436
17.3.3 Perona-Malik Filter for Color Images..... 438
17.3.4 Geometry Preserving Anisotropic Diffusion. . 441
17.3.5 Tschumperle-Deriche Algorithm ............ 444
17.4 Java Implementation.............................. 448
17.5 Exercises........................................ 450
18 Introduction to Spectral Techniques............. 453
18.1 The Fourier Transform............................ 454
18.1.1 Sine and Cosine Functions................. 454
18.1.2 Fourier Series Representation of Periodic
Functions................................. 457
18.1.3 Fourier Integral.......................... 457
18.1.4 Fourier Spectrum and Transformation....... 458
18.1.5 Fourier Transform Pairs................... 459
18.1.6 Important Properties of the Fourier Transform 460
18.2 Working with Discrete Signals.................... 464
18.2.1 Sampling.................................. 464
18.2.2 Discrete and Periodic Functions .......... 469
18.3 The Discrete Fourier Transform (DFT)............. 469
18.3.1 Definition of the DFT..................... 469
18.3.2 Discrete Basis Functions.................. 472
18.3.3 Aliasing Again!........................... 472
18.3.4 Units in Signal and Frequency Space....... 475
18.3.5 Power Spectrum............................ 477
18.4 Implementing the DFT............................. 477
18.4.1 Direct Implementation .................... 477
18.4.2 Fast Fourier Transform (FFT).............. 479
18.5 Exercises ....................................... 479
19 The Discrete Fourier Transform in 2D................ 481
19.1 Definition of the 2D DFT......................... 481
19.1.1 2D Basis Functions ....................... 481
19.1.2 Implementing the 2D DFT................... 482
19.2 Visualizing the 2D Fourier Transform............. 485
19.2.1 Range of Spectral Values ................. 485
19.2.2 Centered Representation of the DFT
Spectrum.................................. 485
19.3 Frequencies and Orientation in 2D................ 486
19.3.1 Effective Frequency....................... 486
19.3.2 Frequency Limits and Aliasing in 2D....... 487
19.3.3 Orientation............................... 488
19.3.4 Normalizing the Geometry of the 2D
Spectrum.................................. 488
19.3.5 Effects of Periodicity.................... 489
19.3.6 Windowing ................................ 490
19.3.7 Common Windowing Functions................ 491
------- 19.4 2D Fourier Transform Examples........................................ 492
XVIII
19.5 Applications of the DFT........................... 496 Contents
19.5.1 Linear Filter Operations in Frequency Space 496
19.5.2 Linear Convolution and Correlation......... 499
19.5.3 Inverse Filters............................ 499
19.6 Exercises ........................................ 500
20 The Discrete Cosine Transform (DCT) .................. 503
20.1 ID DCT............................................ 503
20.1-1 DCT Basis Functions........................ 504
20.1.2 Implementing the ID DCT.................... 504
20.2 2D DCT............................................ 504
20.2.1 Examples................................... 506
20.2.2 Separability............................... 507
20.3 Java Implementation............................... 509
20.4 Other Spectral Transforms........................ 510
20.5 Exercises ........................................ 510
21 Geometric Operations................................. 513
21.1 2D Coordinate Transformations..................... 514
21.1.1 Simple Geometric Mappings.................. 514
21.1.2 Homogeneous Coordinates.................... 515
21.1.3 Affine (Three-Point) Mapping............... 516
21.1.4 Projective (Four-Point) Mapping ........... 519
21.1.5 Bilinear Mapping........................... 525
21.1.6 Other Nonlinear Image Transfer mat ions .... 526
21.1.7 Piecewise Image Transformations............ 528
21.2 Resampling the Image.............................. 529
21.2.1 Source-to-Target Mapping................... 530
21.2.2 Target-to-Source Mapping................... 530
21.3 Java Implementation............................... 531
21.3.1 General Mappings (Class Mapping)........... 532
21.3.2 Linear Mappings............................ 532
21.3.3 Nonlinear Mappings......................... 533
21.3.4 Sample Applications ....................... 533
21.4 Exercises ........................................ 534
22 Pixel Interpolation ................................... 539
22.1 Simple Interpolation Methods...................... 539
22.1.1 Ideal Low-Pass Filter...................... 540
22.2 Interpolation by Convolution...................... 543
22.3 Cubic Interpolation............................... 544
22.4 Spline Interpolation.............................. 546
22.4.1 Catmull-Rom Interpolation.................. 546
22.4.2 Cubic B-spline Approximation............... 547
22.4.3 Mitchell-Netravali Approximation........... 547
22.4.4 Lanczos Interpolation...................... 548
22.5 Interpolation in 2D .............................. 549
22.5.1 Nearest-Neighbor Interpolation in 2D ...... 550
22.5.2 Bilinear Interpolation .................... 551
22.5.3 Bicubic and Spline Interpolation in 2D.... 553
22.5.4 Lanczos Interpolation in 2D................ 554
22.5.5 Examples and Discussion.................... 555
XIX
XX
Contents 22.6 Aliasing........................................ 556
22.6.1 Sampling the Interpolated Image ......... 557
22.6.2 Low-Pass Filtering....................... 558
22.7 Java Implementation............................. 560
22.8 Exercises ...................................... 563
23 Image Matching and Registration...................... 565
23.1 Template Matching in Intensity Images........... 566
23.1.1 Distance between Image Patterns.......... 566
23.1.2 Matching Under Rotation and Scaling..... 574
23.1.3 Java Implementation...................... 574
23.2 Matching Binary Images.......................... 574
23.2.1 Direct Comparison of Binary Images....... 576
23.2.2 The Distance Transform................... 576
23.2.3 Chamfer Matching......................... 580
23.2.4 Java Implementation...................... 582
23.3 Exercises ...................................... 583
24 Non-Rigid Image Matching............................. 587
24.1 The Lucas-Kanade Technique ..................... 587
24.1.1 Registration in ID....................... 587
24.1.2 Extension to Multi-Dimensional Functions . . 589
24.2 The Lucas-Kanade Algorithm...................... 590
24.2.1 Summary of the Algorithm................. 593
24.3 Inverse Compositional Algorithm ................ 595
24.4 Parameter Setups for Various Linear Transformations 598
24.4.1 Pure Translation......................... 598
24.4.2 Affine Transformation.................... 599
24.4.3 Projective Transformation ............... 601
24.4.4 Concatenating Linear Transformations .... 601
24.5 Example......................................... 602
24.6 Java Implementation............................. 603
24.6.1 Application Example...................... 605
24.7 Exercises ...................................... 607
25 Scale-Invariant Feature Transform (SIFT) ............ 609
25.1 Interest Points at Multiple Scales.............. 610
25.1.1 The LoG Filter........................... 610
25.1.2 Gaussian Scale Space..................... 615
25.1.3 LoG/DoG Scale Space...................... 619
25.1.4 Hierarchical Scale Space................. 620
25.1.5 Scale Space Structure in SIFT............ 624
25.2 Key Point Selection and Refinement.............. 630
25.2.1 Local Extrema Detection.................. 630
25.2.2 Position Refinement...................... 632
25.2.3 Suppressing Responses to Edge-Like
Structures................................ 634
25.3 Creating Local Descriptors...................... 636
25.3.1 Finding Dominant Orientations............ 637
25.3.2 SIFT Descriptor Construction............. 640
25.4 SIFT Algorithm Summary.......................... 647
25.5 Matching SIFT Features.......................... 648
25.5.1 Feature Distance and Match Quality.............. 648 Contents
25.5.2 Examples........................................ 654
25.6 Efficient Feature Matching............................. 657
25.7 Java Implementation.................................... 661
25.7.1 SIFT Feature Extraction ........................ 662
25.7.2 SIFT Feature Matching........................... 663
25.8 Exercises ............................................. 663
26 Fourier Shape Descriptors.............................. 665
26.1 Closed Curves in the Complex Plane..................... 665
26.1.1 Discrete 2D Curves.............................. 665
26.2 Discrete Fourier Transform (DFT)....................... 667
26.2.1 Forward Fourier Transform ...................... 668
26.2.2 Inverse Fourier Transform (Reconstruction) . 668
26.2.3 Periodicity of the DFT Spectrum................. 670
26.2.4 Truncating the DFT Spectrum..................... 672
26.3 Geometric Interpretation of Fourier Coefficients. . . . 673
26.3.1 Coefficient G0 Corresponds to the Contour’s
Centroid........................................ 673
26.3.2 Coefficient G1 Corresponds to a Circle.... 674
26.3.3 Coefficient Gni Corresponds to a Circle with
Frequency m..................................... 675
26.3.4 Negative Frequencies............................ 676
26.3.5 Fourier Descriptor Pairs Correspond to
Ellipses........................................ 676
26.3.6 Shape Reconstruction from. Truncated
Fourier Descriptors ............................ 679
26.3.7 Fourier Descriptors from Unsampled Polygons 682
26.4 Effects of Geometric Transformations................... 687
26.4.1 Translation..................................... 687
26.4.2 Scale Change.................................... 688
26.4.3 Rotation........................................ 688
26.4.4 Shifting the Sampling Start Position ........... 689
26.4.5 Effects of Phase Removal........................ 690
26.4.6 Direction of Contour Traversal ................. 691
26.4.7 Reflection (Symmetry) .......................... 691
26.5 ’Transformation-Invariant Fourier Descriptors.... 692
26.5.1 Scale Invariance................................ 693
26.5.2 Start Point Invariance.......................... 694
26.5.3 Rotation Invariance............................. 696
26.5.4 Other Approaches ............................... 697
26.6 Shape Matching with Fourier Descriptors................ 700
26.6.1 Magnitude-Only Matching......................... 700
26.6.2 Complex (Phase-Preserving) Matching....... 701
26.7 Java Implementation.................................... 704
26.8 Discussion and Further Reading......................... 708
26.9 Exercises ............................................. 709
A Mathematical Symbols and Notation ....................... 713
A.l Symbols................................................ 713
A.2 Set Operators.......................................... 717
A.3 Complex Numbers........................................ 717
XXI
Contents b Linear Algebra ......................................... 719
B.l Vectors and Matrices.............................. 719
B.1.1 Column and Row Vectors..................... 720
B.l.2 Length (Norm) of a Vector.................. 720
B.2 Matrix Multiplication............................. 720
B.2.1 Scalar Multiplication....................... 720
B.2.2 Product of Two Matrices .................... 721
B.2.3 Matrix-Vector Products ..................... 721
B.3 Vector Products................................... 722
B.3.1 Dot (Scalar) Product........................ 722
B.3.2 Outer Product .............................. 723
B.3.3 Cross Product............................... 723
B.4 Eigenvectors and Eigenvalues...................... 723
B.4.1 Calculation of Eigenvalues.................. 724
B.5 Homogeneous Coordinates........................... 726
B.6 Basic Matrix-Vector Operations with the Apache
Commons Math Library.............................. 727
B.6.1 Vectors and Matrices........................ 727
B.6.2 Matrix-Vector Multiplication................ 728
B.6.3 Vector Products............................. 728
B.6.4 Inverse of a Square Matrix.................. 728
B.6.5 Eigenvalues and Eigenvectors................ 728
B. 7 Solving Systems of Linear Equations............... 729
B.7.1 Exact Solutions............................. 730
B. 7.2 Over-Determined System (Least-Squares
Solutions)................................. 731
C Calculus................................................ 733
C. l Parabolic Fitting ................................ 733
C. 1.1 Fitting a Parabolic Function to Three
Sample Points.............................. 733
C.l.2 Locating Extrema by Quadratic Interpolation 734
C.2 Scalar and Vector Fields........................... 735
C.2.1 The Jacobian Matrix......................... 736
C.2.2 Gradients................................... 736
C.2.3 Maximum Gradient Direction.................. 737
C.2.4 Divergence of a Vector Field ............... 737
C.2.5 Laplacian Operator.......................... 738
C.2.6 The Hessian Matrix.......................... 738
C. 3 Operations on Multi-Variable, Scalar Functions
(Scalar Fields)................................... 739
C.3.1 Estimating the Derivatives of a Discrete
Function................................... 739
C.3.2 Taylor Series Expansion of Functions........ 740
C. 3.3 Finding the Continuous Extremum of a
Multi-Variable Discrete Function .......... 743
D Statistical Prerequisites 749
D. l Mean, Variance, and Covariance................... 749
D. 1.1 Mean ..................................... 749
D.l.2 Variance and Covariance..................... 749
D.l.3 Biased vs. Unbiased Variance................ 750
XXII
D.2 The Covariance Matrix........................... 750
D.2.1 Example................................... 751
D.2.2 Practical Calculation..................... 752
D.3 Mahalanobis Distance............................ 752
D.3.1 Definition................................ 752
D.3.2 Relation to the Euclidean Distance........ 753
D.3.3 Numerical Aspects......................... 753
D.3.4 Pre-Mapping Data for Efficient Mahalanobis
Matching.................................. 754
D. 4 The Gaussian Distribution....................... 756
D.4.1 Maximum Likelihood Estimation ............ 756
D.4.2 Gaussian Mixtures......................... 758
D.4.3 Creating Gaussian Noise................... 758
E Gaussian Filters........................................ 761
E. l Cascading Gaussian Filters...................... 761
E.2 Gaussian Filters and Scale Space ............... 761
E.3 Effects of Gaussian Filtering in the Frequency
Domain.......................................... 762
E. 4 LoG Approximation by the DoG.................... 763
F Java Notes ............................................. 765
F. l Arithmetic...................................... 765
F.1.1 Integer Division ......................... 765
F.l.2 Modulus Operator.......................... 766
F.l.3 Unsigned Byte Data ....................... 767
F.l.4 Mathematical Functions in Class Math..... 768
F.l.5 Numerical Rounding........................ 769
F.l.6 Inverse Tangent Function.................. 769
F.l.7 Classes Float and Double ................. 770
F.l.8 Testing Floating-Point Values Against Zero . 770
F.2 Arrays in Java.................................. 771
F.2.1 Creating Arrays........................... 771
F.2.2 Array Size................................ 771
F.2.3 Accessing Array Elements.................. 771
F.2.4 2D Arrays................................. 772
F.2.5 Arrays of Objects......................... 775
F.2.6 Searching for Minimum and Maximum Values 775
F.2.7 Sorting Arrays ........................... 776
References............................................... 777
Index.................................................... 791
Contents
XXIII
Wilhelm Burger • Mark J. Burge
Digital image Processing
An Algorithmic Introduction Using Java, Second Edition
This modern, self-contained textbook provides an accessible introduction to the field
from the perspective of a practicing programmer, supporting a detailed presentation
of the fundamental concepts and techniques with practical exercises and fully worked
out implementation examples. This much-anticipated new edition of the definitive
textbook on Digital Image Processing has been completely revised and expanded with
new content and improved teaching material.
Topics and features:
• Contains new chapters on automatic thresholding, filters and edge detection for
color images, edge-preserving smoothing filters, non-rigid image matching, and
Fourier shape descriptors.
• Includes exercises at the end of every chapter, and provides additional supplementary
material at an associated website.
• Uses ImageJ for all examples, a widely used open source imaging system that can
run on all major platforms and be easily ported to other programming languages.
• Describes each solution in a stepwise manner in mathematical form, as abstract
pseudocode algorithms, and as complete Java programs.
• Presents suggested outlines for a one- or two-semester course in the preface.
Advanced undergraduate and graduate students will find this comprehensive and
example-rich textbook will serve as the ideal introduction to digital image processing.
It will also prove invaluable to researchers and professionals seeking a practically
focused self-study primer.
Dr. Wilhelm Burger is a faculty member of the University of Applied Sciences Upper
Austria, Hagenberg, where he serves as Director of the Digital Media degree programs
at the School of Informatics, Communications and Media. Dr. Mark J. Burge is a scientist
at the non-profit organization Noblis in Falls Church, VA, USA. His other publications
include the Handbook of Iris Recognition.
|
| any_adam_object | 1 |
| author | Burger, Wilhelm Burge, Mark James |
| author_GND | (DE-588)132219336 |
| author_facet | Burger, Wilhelm Burge, Mark James |
| author_role | aut aut |
| author_sort | Burger, Wilhelm |
| author_variant | w b wb m j b mj mjb |
| building | Verbundindex |
| bvnumber | BV043519426 |
| classification_rvk | ST 250 ST 320 ST 330 ZN 6050 |
| ctrlnum | (OCoLC)949974616 (DE-599)BVBBV043519426 |
| dewey-full | 006.6 006.37 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.6 006.37 |
| dewey-search | 006.6 006.37 |
| dewey-sort | 16.6 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | Second edition |
| format | Book |
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| spelling | Burger, Wilhelm Verfasser aut Digital image processing an algorithmic introduction using Java Wilhelm Burger, Mark J. Burge Second edition London Springer [2016] © 2016 XXIII, 809 Seiten Illustrationen (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Texts in computer science 1868-0941 Computer science Computational intelligence Image Processing and Computer Vision Signal, Image and Speech Processing Informatik Informatik (DE-588)4026894-9 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Java (DE-588)4028527-3 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Bildverarbeitung (DE-588)4006684-8 s Informatik (DE-588)4026894-9 s Soft Computing (DE-588)4455833-8 s DE-604 Java (DE-588)4028527-3 g 1\p DE-604 Burge, Mark James Verfasser (DE-588)132219336 aut Erscheint auch als Online-Ausgabe 978-1-4471-6684-9 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028935397&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028935397&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Burger, Wilhelm Burge, Mark James Digital image processing an algorithmic introduction using Java Computer science Computational intelligence Image Processing and Computer Vision Signal, Image and Speech Processing Informatik Informatik (DE-588)4026894-9 gnd Soft Computing (DE-588)4455833-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| subject_GND | (DE-588)4026894-9 (DE-588)4455833-8 (DE-588)4006684-8 (DE-588)4028527-3 (DE-588)4123623-3 |
| title | Digital image processing an algorithmic introduction using Java |
| title_auth | Digital image processing an algorithmic introduction using Java |
| title_exact_search | Digital image processing an algorithmic introduction using Java |
| title_full | Digital image processing an algorithmic introduction using Java Wilhelm Burger, Mark J. Burge |
| title_fullStr | Digital image processing an algorithmic introduction using Java Wilhelm Burger, Mark J. Burge |
| title_full_unstemmed | Digital image processing an algorithmic introduction using Java Wilhelm Burger, Mark J. Burge |
| title_short | Digital image processing |
| title_sort | digital image processing an algorithmic introduction using java |
| title_sub | an algorithmic introduction using Java |
| topic | Computer science Computational intelligence Image Processing and Computer Vision Signal, Image and Speech Processing Informatik Informatik (DE-588)4026894-9 gnd Soft Computing (DE-588)4455833-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| topic_facet | Computer science Computational intelligence Image Processing and Computer Vision Signal, Image and Speech Processing Informatik Soft Computing Bildverarbeitung Java Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028935397&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028935397&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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