Practical Introduction to Computer Vision with OpenCV

Häftad, Engelska, 2014

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Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV librariesComputer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games consoles; cameras imaging difficult modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This book is concerned with helping the next generation of computer developers to make use of all these images in order to develop systems which are more intuitive and interact with us in more intelligent ways. Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV librariesOffers an introduction to computer vision, with enough theory to make clear how the various algorithms work but with an emphasis on practical programming issuesProvides enough material for a one semester course in computer vision at senior undergraduate and Masters levels Includes the basics of cameras and images and image processing to remove noise, before moving on to topics such as image histogramming; binary imaging; video processing to detect and model moving objects; geometric operations & camera models; edge detection; features detection; recognition in imagesContains a large number of vision application problems to provide students with the opportunity to solve real problems. Images or videos for these problems are provided in the resources associated with this book which include an enhanced eBook

Produktinformation

Utgivningsdatum2014-05-13
Mått170 x 245 x 13 mm
Vikt476 g
FormatHäftad
SpråkEngelska
Antal sidor240
FörlagJohn Wiley & Sons Inc
ISBN9781118848456

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Kenneth Dawson-Howe, School of Computer Science and Statistics, Trinity College Dublin, IrelandDr. Dawson-Howe is a Lecturer in the School of Computer Science and Statistics and part of the Graphics, Vision and Visualisation (GV2) Research Group at Trinity College Dublin. He currently teaches the course Computer Vision/Vision Systems to final year undergraduate and Masters students. He has been teaching courses in the area of computer vision for over 20 years. He is co-author of the Dictionary of Computer Vision & Image Processing published by Wiley in 2005 (2nd Edition to publish December 2013).

Preface xiii 1 Introduction 11.1 A Difficult Problem 11.2 The Human Vision System 21.3 Practical Applications of Computer Vision 31.4 The Future of Computer Vision 51.5 Material in This Textbook 61.6 Going Further with Computer Vision 72 Images 92.1 Cameras 92.1.1 The Simple Pinhole Camera Model 92.2 Images 102.2.1 Sampling 112.2.2 Quantisation 112.3 Colour Images 132.3.1 Red–Green–Blue (RGB) Images 142.3.2 Cyan–Magenta–Yellow (CMY) Images 172.3.3 YUV Images 172.3.4 Hue Luminance Saturation (HLS) Images 182.3.5 Other Colour Spaces 202.3.6 Some Colour Applications 202.4 Noise 222.4.1 Types of Noise 232.4.2 Noise Models 252.4.3 Noise Generation 262.4.4 Noise Evaluation 262.5 Smoothing 272.5.1 Image Averaging 272.5.2 Local Averaging and Gaussian Smoothing 282.5.3 Rotating Mask 302.5.4 Median Filter 313 Histograms 353.1 1D Histograms 353.1.1 Histogram Smoothing 363.1.2 Colour Histograms 373.2 3D Histograms 393.3 Histogram/Image Equalisation 403.4 Histogram Comparison 413.5 Back-projection 433.6 k-means Clustering 444 Binary Vision 494.1 Thresholding 494.1.1 Thresholding Problems 504.2 Threshold Detection Methods 514.2.1 Bimodal Histogram Analysis 524.2.2 Optimal Thresholding 524.2.3 Otsu Thresholding 544.3 Variations on Thresholding 564.3.1 Adaptive Thresholding 564.3.2 Band Thresholding 574.3.3 Semi-thresholding 584.3.4 Multispectral Thresholding 584.4 Mathematical Morphology 594.4.1 Dilation 604.4.2 Erosion 624.4.3 Opening and Closing 634.4.4 Grey-scale and Colour Morphology 654.5 Connectivity 664.5.1 Connectedness: Paradoxes and Solutions 664.5.2 Connected Components Analysis 675 Geometric Transformations 715.1 Problem Specification and Algorithm 715.2 Affine Transformations 735.2.1 Known Affine Transformations 745.2.2 Unknown Affine Transformations 755.3 Perspective Transformations 765.4 Specification of More Complex Transformations 785.5 Interpolation 785.5.1 Nearest Neighbour Interpolation 795.5.2 Bilinear Interpolation 795.5.3 Bi-Cubic Interpolation 805.6 Modelling and Removing Distortion from Cameras 805.6.1 Camera Distortions 815.6.2 Camera Calibration and Removing Distortion 826 Edges 836.1 Edge Detection 836.1.1 First Derivative Edge Detectors 856.1.2 Second Derivative Edge Detectors 926.1.3 Multispectral Edge Detection 976.1.4 Image Sharpening 986.2 Contour Segmentation 996.2.1 Basic Representations of Edge Data 996.2.2 Border Detection 1026.2.3 Extracting Line Segment Representations of Edge Contours 1056.3 Hough Transform 1086.3.1 Hough for Lines 1096.3.2 Hough for Circles 1116.3.3 Generalised Hough 1127 Features 1157.1 Moravec Corner Detection 1177.2 Harris Corner Detection 1187.3 FAST Corner Detection 1217.4 SIFT 1227.4.1 Scale Space Extrema Detection 1237.4.2 Accurate Keypoint Location 1247.4.3 Keypoint Orientation Assignment 1267.4.4 Keypoint Descriptor 1277.4.5 Matching Keypoints 1277.4.6 Recognition 1277.5 Other Detectors 1297.5.1 Minimum Eigenvalues 1307.5.2 SURF 1308 Recognition 1318.1 Template Matching 1318.1.1 Applications 1318.1.2 Template Matching Algorithm 1338.1.3 Matching Metrics 1348.1.4 Finding Local Maxima or Minima 1358.1.5 Control Strategies for Matching 1378.2 Chamfer Matching 1378.2.1 Chamfering Algorithm 1378.2.2 Chamfer Matching Algorithm 1398.3 Statistical Pattern Recognition 1408.3.1 Probability Review 1428.3.2 Sample Features 1438.3.3 Statistical Pattern Recognition Technique 1498.4 Cascade of Haar Classifiers 1528.4.1 Features 1548.4.2 Training 1568.4.3 Classifiers 1568.4.4 Recognition 1588.5 Other Recognition Techniques 1588.5.1 Support Vector Machines (SVM) 1588.5.2 Histogram of Oriented Gradients (HoG) 1598.6 Performance 1608.6.1 Image and Video Datasets 1608.6.2 Ground Truth 1618.6.3 Metrics for Assessing Classification Performance 1628.6.4 Improving Computation Time 1659 Video 1679.1 Moving Object Detection 1679.1.1 Object of Interest 1689.1.2 Common Problems 1689.1.3 Difference Images 1699.1.4 Background Models 1719.1.5 Shadow Detection 1799.2 Tracking 1809.2.1 Exhaustive Search 1819.2.2 Mean Shift 1819.2.3 Dense Optical Flow 1829.2.4 Feature Based Optical Flow 1859.3 Performance 1869.3.1 Video Datasets (and Formats) 1869.3.2 Metrics for Assessing Video Tracking Performance 18710 Vision Problems 18910.1 Baby Food 18910.2 Labels on Glue 19010.3 O-rings 19110.4 Staying in Lane 19210.5 Reading Notices 19310.6 Mailboxes 19410.7 Abandoned and Removed Object Detection 19510.8 Surveillance 19610.9 Traffic Lights 19710.10 Real Time Face Tracking 19810.11 Playing Pool 19910.12 Open Windows 20010.13 Modelling Doors 20110.14 Determining the Time from Analogue Clocks 20210.15 Which Page 20310.16 Nut/Bolt/Washer Classification 20410.17 Road Sign Recognition 20510.18 License Plates 20610.19 Counting Bicycles 20710.20 Recognise Paintings 208References 209Index 213