A practical introduction to computer vision with OpenCV:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
[2014]
|
| Ausgabe: | This edition first published 2014 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XV, 217 Seiten Illustrationen |
| ISBN: | 9781118848456 1118848454 |
Internformat
MARC
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| 100 | 1 | |a Dawson-Howe, Kenneth |0 (DE-588)1054051968 |4 aut | |
| 245 | 1 | 0 | |a A practical introduction to computer vision with OpenCV |c Kenneth Dawson-Howe |
| 250 | |a This edition first published 2014 | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c [2014] | |
| 300 | |a XV, 217 Seiten |b Illustrationen | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Preface xiii
1 Introduction 1
1.1 A Difficult Problem 1
1.2 The Human Vision System 2
1.3 Practical Applications of Computer Vision 3
1.4 The Future of Computer Vision 5
1.5 Material in This Textbook 6
1.6 Going Further with Computer Vision 7
2 Images 9
2.1 Cameras 9
2.1.1 The Simple Pinhole Camera Model 9
2.2 Images 10
2.2.1 Sampling 11
2.2.2 Quantisation 11
2.3 Colour Images 13
2.3.1 Red-Green-Blue (RGB) Images 14
2.3.2 Cyan-Magenta-Yellow (CMY) Images 17
2.3.3 YUV Images 17
2.3.4 Hue Luminance Saturation (HLS) Images 18
2.3.5 Other Colour Spaces 20
2.3.6 Some Colour Applications 20
2.4 Noise 22
2.4.1 Types of Noise 23
2.4.2 Noise Models 25
2.4.3 Noise Generation 26
2.4.4 Noise Evaluation 26
2.5 Smoothing 27
2.5.1 Image Averaging 27
2.5.2 Local Averaging and Gaussian Smoothing 28
2.5.3 Rotating Mask 30
2.5.4 Median Filter 31
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Histograms
ID Histograms
3. /. 1 Histogram Smoothing
3.1.2 Colour Histograms
3D Histograms
Histogram/Image Equalisation
Histogram Comparison
Back-projection
k-means Clustering
Binary Vision
Thresholding
4. J. 1 Thresholding Problems
Threshold Detection Methods
4.2.1 Bimodal Histogram Analysis
4.2.2 Optimal Thresholding
4.2.3 Otsu Thresholding
Variations on Thresholding
4.3. J Adaptive Thresholding
4.3.2 Band Thresholding
4.3.3 Semi-thresholding
4.3.4 Multispectral Thresholding
Mathematical Morphology
4.4.1 Dilation
4.4.2 Erosion
4.4.3 Opening and Closing
4.4.4 Grey-scale and Colour Morphology
Connectivity
4.5.1 Connectedness: Paradoxes and Solutions
4.5.2 Connected Components Analysis
Geometric Transformations
Problem Specification and Algorithm
Affine Transformations
5.2.1 Known Affine Transformations
5.2.2 Unknown Affine Transformations
Perspective Transformations
Specification of More Complex Transformations
Interpolation
5.5.1 Nearest Neighbour Interpolation
5.5.2 Bilinear Interpolation
5.5.3 Bi-Cubic Interpolation
Modelling and Removing Distortion from Cameras
5.6.1 Camera Distortions
5.6.2 Camera Calibration and Removing Distortion
Contents ix
6 Edges 83
6.1 Edge Detection 83
6.1.1 First Derivative Edge Detectors 85
6.1.2 Second Derivative Edge Detectors 92
6.1.3 Multispectral Edge Detection 97
6.1.4 ¡mage Sharpening 98
6.2 Contour Segmentation 99
6.2.1 Basic Representations of Edge Data 99
6.2.2 Border Detection 102
6.2.3 Extracting Line Segment Representations of Edge Contours 105
6.3 Hough Transform 108
6.3.1 Hough for Lines 109
6.3.2 Hough for Circles 111
6.3.3 Generalised Hough 112
7 Features 115
7.1 Moravec Corner Detection 117
7.2 Harris Corner Detection 118
7.3 FAST Corner Detection 121
7.4 SIFT 122
7.4.1 Scale Space Extrema Detection 123
7.4.2 Accurate Keypoint Location 124
7.4.3 Keypoint Orientation Assignment 126
7.4.4 Keypoint Descriptor 122
7.4.5 Matching Key points 122
7.4.6 Recognition 122
2.5 Other Detectors 129
7.5.1 Minimum Eigenvalues 130
7.5.2 SURF 130
8 Recognition 131
8.1 Template Matching 131
8.1.1 Applications 131
8.1.2 Template Matching Algorithm 133
8.1.3 Matching Metrics 134
8.1.4 Finding Local Maxima or Minima 135
8.1.5 Control Strategies for Matching 132
8.2 Chamfer Matching 132
8.2.1 Chamfering Algorithm 132
8.2.2 Chamfer Matching Algorithm 139
8.3 Statistical Pattern Recognition 140
8.3.1 Probability Review 142
8.3.2 Sample Features 143
8.3.3 Statistical Pattern Recognition Technique 149
8.4 Cascade of Haar Classifiers 132
8.4.1 Features 134
8.4.2 Training 136
X
( on tents
8.4.3 Classifiers 10
8.4.4 Recognition ÍO
8.5 Other Recognition Techniques * ^
8.5.1 Support Vector Machines (S VM) *
8.5.2 Histogram of Oriented Gradients (HoG) I ^
8.6 Performance ^
8.6.1 Image and Video Datasets 160
8.6.2 Ground Truth 161
8.6.3 Metrics for Assessing Classification Performance 162
8.6.4 Improving Computation Time 165
9 Video 167
9.1 Moving Object Detection 167
9.1.1 Object of Interest 168
9.1.2 Common Problems 168
9.1.3 Difference Images 169
9.1.4 Background Models 171
9.1.5 Shadow Detection 179
9.2 Tracking 180
9.2.1 Exhaustive Search 181
9.2.2 Mean Shift 181
9.2.3 Dense Optical Flow 182
9.2.4 Feature Based Optical Flow 185
9.3 Performance 186
9.3.1 Video Datasets (and Formats) 186
9.3.2 Metrics for Assessing Video Tracking Performance 187
10 Vision Problems jg9
10.1 Baby Food 1^9
10.2 Labels on Glue I9q
10.3 O-rings 191
10.4 Staying in Lane j92
10.5 Reading Notices 193
10.6 Mailboxes 19^
10.7 Abandoned and Removed Object Detection 195
10.8 Surveillance ^6
10.9 Traffic Lights I ^
10.10 Real Time Face Tracking 1^
10.11 Playing Pool
10.12 Open Windows 2m
10.13 Modelling Doors
10.14 Determining the Time from Analogue Clocks 900
10.15 Which Page ^3
10.16 Nut/Bolt/Washer Classification 204
10.17 Road Sign Recognition
10.18 License Plates ^,
Contents
XI
10.19 Counting Bicycles 207
10.20 Recognise Paintings 208
References 209
Index 213
|
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| discipline | Informatik |
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| id | DE-604.BV041825285 |
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| isbn | 9781118848456 1118848454 |
| language | English |
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| spelling | Dawson-Howe, Kenneth (DE-588)1054051968 aut A practical introduction to computer vision with OpenCV Kenneth Dawson-Howe This edition first published 2014 Chichester [u.a.] Wiley [2014] XV, 217 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Computer vision Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf OpenCV (DE-588)1038770092 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s OpenCV (DE-588)1038770092 s DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027270273&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Dawson-Howe, Kenneth A practical introduction to computer vision with OpenCV Computer vision Maschinelles Sehen (DE-588)4129594-8 gnd OpenCV (DE-588)1038770092 gnd |
| subject_GND | (DE-588)4129594-8 (DE-588)1038770092 |
| title | A practical introduction to computer vision with OpenCV |
| title_auth | A practical introduction to computer vision with OpenCV |
| title_exact_search | A practical introduction to computer vision with OpenCV |
| title_full | A practical introduction to computer vision with OpenCV Kenneth Dawson-Howe |
| title_fullStr | A practical introduction to computer vision with OpenCV Kenneth Dawson-Howe |
| title_full_unstemmed | A practical introduction to computer vision with OpenCV Kenneth Dawson-Howe |
| title_short | A practical introduction to computer vision with OpenCV |
| title_sort | a practical introduction to computer vision with opencv |
| topic | Computer vision Maschinelles Sehen (DE-588)4129594-8 gnd OpenCV (DE-588)1038770092 gnd |
| topic_facet | Computer vision Maschinelles Sehen OpenCV |
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