Learning OpenCV 3: computer vision in C++ with the OpenCV library
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| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
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Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
December 2016
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| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xxv, 990 Seiten Illustrationen, Diagramme |
| ISBN: | 9781491937990 |
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| adam_text | Learning OpenCV 3
Computer Vision in C++ with
the OpenCV Library
Adrian Kaehler and Gary Brodski
Beijint
ULB Darmstadt
• Tokyo
O’REILLY
19692345
Table of Contents
xv
w 1
Is OpenCV? 1
ses OpenCV? 2
Is Computer Vision? 3
rigin of OpenCV 6
nCV Block Diagram 8
ding Up OpenCV with IPP 9
Owns OpenCV? 10
loading and Installing OpenCV 10
llation 10
g the Latest OpenCV via Git 13
OpenCV Documentation 13
lied Documentation 14
ne Documentation and the Wiki 14
V Contribution Repository 17
nloading and Building Contributed Modules 17
ility 18
ary 19
ses 19
uction to OpenCV 21
e Files 21
urces 22
rogram—Display a Picture 23
d Program—Video 25
g Around 27
iii
Table of Contents
Preface xv
1 Overview 1
What Is OpenCV? 1
Who Uses OpenCV? 2
What Is Computer Vision? 3
The Origin of OpenCV 6
OpenCV Block Diagram 8
Speeding Up OpenCV with IPP 9
Who Owns OpenCV? 10
Downloading and Installing OpenCV 10
Installation 10
Getting the Latest OpenCV via Git 13
More OpenCV Documentation 13
Supplied Documentation 14
Online Documentation and the Wiki 14
OpenCV Contribution Repository 17
Downloading and Building Contributed Modules 17
Portability 18
Summary 19
Exercises 19
2 Introduction to OpenCV 21
Include Files 21
Resources 22
First Program—Display a Picture 23
Second Program—Video 25
Moving Around 27
iii
A Simple Transformation 31
A Not-So-Simple Transformation 32
Input from a Camera 35
Writing to an AVI File 36
Summary 38
Exercises 38
3 Getting to Know OpenCV Data Types 41
The Basics 41
OpenCV Data Types 41
Overview of the Basic Types 42
Basic Types: Getting Down to Details 44
Helper Objects 52
Utility Functions 60
The Template Structures 67
Summary 68
Exercises 69
4 Images and Large Array Types 71
Dynamic and Variable Storage 71
The cv::Mat Class: N-Dimensional Dense Arrays 72
Creating an Array 73
Accessing Array Elements Individually 78
The N-ary Array Iterator: NAryMatlterator 81
Accessing Array Elements by Block 84
Matrix Expressions: Algebra and cv::Mat 85
Saturation Casting 87
More Things an Array Can Do 88
The cv::SparseMat Class: Sparse Arrays 89
Accessing Sparse Array Elements 90
Functions Unique to Sparse Arrays 92
Template Structures for Large Array Types 94
Summary 97
Exercises 97
5 Array Operations 99
More Things You Can Do with Arrays 99
cv::abs() 102
cv::absdiff() 103
cv::add() 103
cv::addWeighted() 104
cv::bitwise_and() 106
iv | Table of Contents
cv::bitwise_not() 107
cv::bitwise_or() 107
cv::bitwise_xor() 108
cv::calcCovarMatrix() 108
cv::cartToPolar() 110
cv::checkRange() 111
cv::compare() 111
cv::completeSymm() 112
cv::convertScaleAbs() 112
cv::countNonZero() 113
cv::cvarrToMat() 113
cv::dct() 114
cv::dft() 115
cv::cvtColor() 117
cv::determinant() 119
cv::divide() 120
cv::eigen() 120
cv::exp() 121
cv::extractImageCOI() 121
cv::flip() 122
cv::gemm() 122
cv::getConvertElem() and cv::getConvertScaleElem() 123
cv::idct() 124
cv::idft() 124
cv::inRange() 124
cv::insertImageCOI() 125
cv::invert() 126
cv::log() 126
cv::LUT() 127
cv::magnitude() 127
cv::Mahalanobis() 128
cv::max() 129
cv::mean() 130
cv::meanStdDev() 130
cv::merge() 131
cv::min() 131
cv::minMaxIdx() 132
cv::minMaxLoc() 133
cv: :mixChannels() 134
cv::mulSpectrums() 136
cv::multiply() 136
cv::mulTransposed() 136
Table of Contents | v
cv::norm() 137
cv::normalize() 139
cv: :perspectiveT ransform() 140
cv::phase() 141
cv: :polarT oCart() 142
cv::pow() 142
cv::randu() 143
cv::randn() 143
cv::randShuffle() 144
cv::reduce() 144
cv::repeat() 145
cv::scaleAdd() 146
cv::setldentity() 146
cv::solve() 147
cv::solveCubic() 148
cv::solvePoly() 149
cv::sort() 149
cv::sortIdx() 149
cv::split() 150
cv::sqrt() 150
cv::subtract() 152
cv::sum() 152
cv::trace() 152
cv::transform() 153
cv::transpose() 153
Summary 154
Exercises 154
Drawing and Annotating 157
Drawing Things 157
Line Art and Filled Polygons 158
Fonts and Text 165
Summary 167
Exercises 167
Functors in OpenCV 169
Objects That “Do Stuff’ 169
Principal Component Analysis (cv::PCA) 169
Singular Value Decomposition (cv::SVD) 173
Random Number Generator (cv::RNG) 176
Summary 179
Exercises 180
vi | Table of Contents
8 Image, Video, and Data Files 183
HighGUI: Portable Graphics Toolkit 183
Working with Image Files 185
Loading and Saving Images 185
A Note About Codecs 188
Compression and Decompression 188
Working with Video 189
Reading Video with the cv::VideoCapture Object 190
Writing Video with the cv::VideoWriter Object 196
Data Persistence 198
Writing to a cv::FileStorage 198
Reading from a cv::FileStorage 200
cv::FileNode 201
Summary 204
Exercises 204
9 Cross-Platform and Native Windows 207
Working with Windows 207
HighGUI Native Graphical User Interface 208
Working with the Qt Backend 220
Integrating OpenCV with Full GUI Toolkits 232
Summary 247
Exercises 247
10 Filters and Convolution 249
Overview 249
Before We Begin 249
Filters, Kernels, and Convolution 249
Border Extrapolation and Boundary Conditions 251
Threshold Operations 255
Otsu’s Algorithm 258
Adaptive Threshold 259
Smoothing 261
Simple Blur and the Box Filter 262
Median Filter 265
Gaussian Filter 266
Bilateral Filter 267
Derivatives and Gradients 269
The Sobel Derivative 269
Scharr Filter 272
The Laplacian 273
Image Morphology 275
Table of Contents | vii
Dilation and Erosion 276
The General Morphology Function 281
Opening and Closing 281
Morphological Gradient 285
Top Hat and Black Hat 287
Making Your Own Kernel 289
Convolution with an Arbitrary Linear Filter 290
Applying a General Filter with cv::filter2D() 291
Applying a General Separable Filter with cv::sepFilter2D 292
Kernel Builders 292
Summary 294
Exercises 294
11 General Image Transforms 299
Overview 299
Stretch, Shrink, Warp, and Rotate 299
Uniform Resize 300
Image Pyramids 302
Nonuniform Mappings 306
Affine Transformation 308
Perspective Transformation 313
General Remappings 316
Polar Mappings 317
LogPolar 318
Arbitrary Mappings 322
Image Repair 323
Inpainting 324
Denoising 325
Histogram Equalization 328
cv::equalizeHist(): Contrast equalization 331
Summary 331
Exercises 332
12 Image Analysis 335
Overview 335
Discrete Fourier Transform 336
cv::dft(): The Discrete Fourier Transform 336
cv::idft(): The Inverse Discrete Fourier Transform 339
cv::mulSpectrums(): Spectrum Multiplication 339
Convolution Using Discrete Fourier Transforms 340
cv::dct(): The Discrete Cosine Transform 342
cv::idct(): The Inverse Discrete Cosine Transform 343
viii | Table of Contents
Integral Images 343
cv::integral() for Standard Summation Integral 346
cv::integral() for Squared Summation Integral 346
cv::integral() for Tilted Summation Integral 346
The Canny Edge Detector 347
cv::Canny() 349
Hough Transforms 349
Hough Line Transform 349
Hough Circle Transform 354
Distance Transformation 358
cv::distanceTransform() for Unlabeled Distance Transform 359
cv::distanceTransform() for Labeled Distance Transform 360
Segmentation 360
Flood Fill 361
Watershed Algorithm 365
Grabcuts 366
Mean-Shift Segmentation 368
Summary 370
Exercises 371
13 Histograms and Templates 373
Histogram Representation in OpenCV 376
cv::calcHist(): Creating a Histogram from Data 377
Basic Manipulations with Histograms 380
Histogram Normalization 380
Histogram Threshold 380
Finding the Most Populated Bin 380
Comparing Two Histograms 382
Histogram Usage Examples 385
Some More Sophisticated Histograms Methods 388
Earth Mover’s Distance 389
Back Projection 394
Template Matching 397
Square Difference Matching Method (cv::TM_SQDIFF) 399
Normalized Square Difference Matching Method
(cv::TM_SQDIFF_NORMED) 400
Correlation Matching Methods (cv::TM_CCORR) 400
Normalized Cross-Correlation Matching Method
(cv::TM_CCORR_NORMED) 400
Correlation Coefficient Matching Methods (cv::TM_CCOEFF) 400
Normalized Correlation Coefficient Matching Method
(cv::TM_CCOEFF_NORMED) 401
Table of Contents | ix
Summary
Exercises
404
404
14 Contours 407
Contour Finding 407
Contour Hierarchies 408
Drawing Contours 413
A Contour Example 414
Another Contour Example 416
Fast Connected Component Analysis 417
More to Do with Contours 420
Polygon Approximations 420
Geometry and Summary Characteristics 421
Geometrical Tests 428
Matching Contours and Images 429
Moments 429
More About Moments 431
Matching and Hu Moments 435
Using Shape Context to Compare Shapes 436
Summary 441
Exercises 442
15 Background Subtraction 445
Overview of Background Subtraction 445
Weaknesses of Background Subtraction 446
Scene Modeling 447
A Slice of Pixels 447
Frame Differencing 451
Averaging Background Method 452
Accumulating Means, Variances, and Covariances 458
A More Advanced Background Subtraction Method 467
Structures 470
Learning the Background 472
Learning with Moving Foreground Objects 474
Background Differencing: Finding Foreground Objects 475
Using the Codebook Background Model 477
A Few More Thoughts on Codebook Models 477
Connected Components for Foreground Cleanup 477
A Quick Test 481
Comparing Two Background Methods 483
OpenCV Background Subtraction Encapsulation 485
The cv::BackgroundSubtractor Base Class 485
x | Table of Contents
KaewTraKuPong and Bowden Method 486
Zivkovic Method 488
Summary 490
Exercises 491
16 Keypoints and Descriptors 493
Keypoints and the Basics of Tracking 493
Corner Finding 494
Introduction to Optical Flow 498
Lucas-Kanade Method for Sparse Optical Flow 500
Generalized Keypoints and Descriptors 511
Optical Flow, Tracking, and Recognition 513
How OpenCV Handles Keypoints and Descriptors, the General Case 514
Core Keypoint Detection Methods 526
Keypoint Filtering 571
Matching Methods 573
Displaying Results 580
Summary 583
Exercises 584
17 Tracking 587
Concepts in Tracking 587
Dense Optical Flow 588
The Farnebäck Polynomial Expansion Algorithm 589
The Dual TV-L1 Algorithm 592
The Simple Flow Algorithm 596
Mean-Shift and Camshift Tracking 600
Mean-Shift 601
Camshift 604
Motion Templates 605
Estimators 613
The Kalman Filter 615
A Brief Note on the Extended Kalman Filter 633
Summary 634
Exercises 634
18 Camera Models and Calibration 637
Camera Model 638
The Basics of Projective Geometry 641
Rodrigues Transform 643
Lens Distortions 644
Calibration 648
Table of Contents | xi
Rotation Matrix and Translation Vector 650
Calibration Boards 652
Homography 660
Camera Calibration 665
Undistortion 677
Undistortion Maps 678
Converting Undistortion Maps Between Representations with
cv::convertMaps() 679
Computing Undistortion Maps with cv::initUndistortRectifyMap() 680
Undistorting an Image with cv::remap() 682
Undistortion with cv::undistort() 683
Sparse Undistortion with cv::undistortPoints() 683
Putting Calibration All Together 684
Summary 687
Exercises 688
19 Projection and Three-Dimensional Vision 691
Projections 692
Affine and Perspective Transformations 694
Bird’s-Eye-View Transform Example 695
Three-Dimensional Pose Estimation 700
Pose Estimation from a Single Camera 700
Stereo Imaging 703
Triangulation 704
Epipolar Geometry 708
The Essential and Fundamental Matrices 710
Computing Epipolar Lines 720
Stereo Calibration 721
Stereo Rectification 726
Stereo Correspondence 737
Stereo Calibration, Rectification, and Correspondence Code Example 752
Depth Maps from Three-Dimensional Reprojection 759
Structure from Motion 761
Fitting Lines in Two and Three Dimensions 762
Summary 765
Exercises 766
20 The Basics of Machine Learning in OpenCV 769
What Is Machine Learning? 770
Training and Test Sets 770
Supervised and Unsupervised Learning 771
Generative and Discriminative Models 773
xii | Table of Contents
OpenCV ML Algorithms 774
Using Machine Learning in Vision 776
Variable Importance 778
Diagnosing Machine Learning Problems 779
Legacy Routines in the ML Library 785
K-Means 786
Mahalanobis Distance 793
Summary 797
Exercises 797
21 StatModel: The Standard Model for Learning in OpenCV 799
Common Routines in the ML Library 799
Training and the cv::ml::TrainData Structure 802
Prediction 809
Machine Learning Algorithms Using cv::StatModel 810
Naive/Normal Bayes Classifier 810
Binary Decision Trees 816
Boosting 830
Random Trees 837
Expectation Maximization 842
K-Nearest Neighbors 846
Multilayer Perceptron 849
Support Vector Machine 859
Summary 870
Exercises 871
22 Object Detection 875
Tree-Based Object Detection Techniques 875
Cascade Classifiers 876
Supervised Learning and Boosting Theory 879
Learning New Objects 888
Object Detection Using Support Vector Machines 897
Latent SVM for Object Detection 898
The Bag of Words Algorithm and Semantic Categorization 901
Summary 907
Exercises 907
23 Future of OpenCV 909
Past and Present 909
OpenCV 3 x 910
How Well Did Our Predictions Go Last Time? 911
Future Functions 912
Table of Contents | xiii
Current GSoC Work 913
Community Contributions 915
OpenCV org 916
Some AI Speculation 917
Afterword 920
A Planar Subdivisions 923
B opencv_contrib 939
C Calibration Patterns 943
Bibliography 949
Index 967
|
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| id | DE-604.BV043718035 |
| illustrated | Illustrated |
| indexdate | 2024-08-01T12:37:08Z |
| institution | BVB |
| isbn | 9781491937990 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029130158 |
| oclc_num | 973023163 |
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| physical | xxv, 990 Seiten Illustrationen, Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | O'Reilly |
| record_format | marc |
| spellingShingle | Kaehler, Adrian 1973- Bradski, Gary R. Learning OpenCV 3 computer vision in C++ with the OpenCV library Programmbibliothek (DE-588)4121521-7 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| subject_GND | (DE-588)4121521-7 (DE-588)4006684-8 |
| title | Learning OpenCV 3 computer vision in C++ with the OpenCV library |
| title_auth | Learning OpenCV 3 computer vision in C++ with the OpenCV library |
| title_exact_search | Learning OpenCV 3 computer vision in C++ with the OpenCV library |
| title_full | Learning OpenCV 3 computer vision in C++ with the OpenCV library Adrian Kaehler and Gary Bradski |
| title_fullStr | Learning OpenCV 3 computer vision in C++ with the OpenCV library Adrian Kaehler and Gary Bradski |
| title_full_unstemmed | Learning OpenCV 3 computer vision in C++ with the OpenCV library Adrian Kaehler and Gary Bradski |
| title_short | Learning OpenCV 3 |
| title_sort | learning opencv 3 computer vision in c with the opencv library |
| title_sub | computer vision in C++ with the OpenCV library |
| topic | Programmbibliothek (DE-588)4121521-7 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| topic_facet | Programmbibliothek Bildverarbeitung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029130158&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kaehleradrian learningopencv3computervisionincwiththeopencvlibrary AT bradskigaryr learningopencv3computervisionincwiththeopencvlibrary |
Inhaltsverzeichnis
THWS Schweinfurt Zentralbibliothek Lesesaal
| Signatur: |
2000 ST 331 K11 |
|---|---|
| Exemplar 1 | ausleihbar Verfügbar Bestellen |