Learn Computer Vision Using OpenCV (eBook)

Table of Contents 5
About the Author 9
About the Technical Reviewer 10
Acknowledgments 11
Foreword 12
Introduction 13
Chapter 1: Artificial Intelligence and Computer Vision 17
Introduction to Artificial Intelligence 19
Natural Language Processing 23
Robotics 26
Machine Learning 27
Expert Systems 29
Speech and Voice Recognition 29
Intelligent Process Automation 30
Introduction to Computer Vision 30
Scope 31
Challenges of Computer Vision 35
Real-World Applications of Computer Vision 37
Automotive Industry 38
Healthcare and Biomedical Industry 38
Retail Industry 39
Images and Their Features 40
Color Spaces 41
Core Building Blocks (Input – Process – Output) 42
Optical Character Recognition and Intelligent Character Recognition 44
Optical Mark Recognition 44
Conclusion 44
Chapter 2: OpenCV with Python 46
About OpenCV 47
Setting Up OpenCV with Python 47
Windows Installation 47
macOS Installation 51
Using Modules 53
Working with Images and Videos 55
Using NumPy 55
Reading and Loading Images with OpenCV and NumPy 56
Working with a Histogram Representation 59
Videos 61
Loading Videos from a Webcam 61
Loading Videos from a File 62
Reading the Video and Writing into a File 63
Conclusion 64
Chapter 3: Deep Learning for Computer Vision 66
Deep Learning: An Overview 67
Deep Learning Applications in Computer Vision 68
Classification 68
Detection and Localization 69
(Semantic) Segmentation 70
Similarity Learning 70
Image Captioning 70
Generative Models 71
Video Analysis 72
Neural Networks at Their Core 72
Artificial Neural Networks 73
Artificial Neurons or Perceptrons 73
Training Neural Networks 77
Backpropagation 77
Gradient Descent and Stochastic Gradient Descent 78
Convolutional Neural Networks 78
Convolution Layer 79
Pooling Layer 80
Fully Connected Layer 80
Recurrent Neural Networks 81
Backpropagation Through Time 83
Conclusion 84
Chapter 4: Image Manipulation and Segmentation 85
Image Manipulations 86
Accessing and Manipulating Pixels 87
Drawing Geometric Shapes or Writing Text on a Color Image 89
Filtering Images 93
Transforming Images 96
Translation 97
Rotation 99
Image Scaling 101
Edge Detection 102
Image Segmentation 104
Line Detection 106
Circle Detection 107
Conclusion 110
Chapter 5: Object Detection and Recognition 111
Basics of Object Detection 111
Object Detection vs. Object Recognition 112
Template Matching 113
Challenges with Template Matching 116
Understanding Image “Features” 116
Interesting and Uninteresting Points 117
Types of Image Features 118
Feature Matching 119
Image Corners As Features 119
Harris Corner Algorithm 120
Feature Tracking and Matching Flow 122
Scale Variant Feature Transform 123
Speeded-Up Robust Features 126
Features from Accelerated Segment Test 127
Binary Robust Independent Elementary Features 128
Oriented FAST and Rotated BRIEF 130
Conclusion 131
Chapter 6: Motion Analysis and Object Tracking 132
Introduction to Object Tracking 133
Challenges of Object Tracking 134
Object Detection Techniques for Tracking 134
Frame Differentiation 135
Background Subtraction 136
Optical Flow 138
Lucas–Kanade Differential Algorithm 139
Dense Optical Flow Algorithm 142
Object Classification 144
Shaped-Based Classification 145
Motion-Based Classification 145
Color-Based Classification 145
Texture-Based Classification 146
Object Tracking Methods 146
Point Tracking Method 147
Kernel-Based Tracking Methods 148
Simple Template Matching 148
Meanshift Method 149
Support Vector Machine 157
Layering-Based Tracking 157
Silhouette-Based Tracking 157
Contour Tracking 158
Shape Matching 158
Conclusion 158
Index 159