Description
Book SynopsisThis textbook offers advanced content on computer vision (basic content can be found in its prerequisite textbook, “2D Computer Vision: Principles, Algorithms and Applications”), including the basic principles, typical methods and practical techniques. It is intended for graduate courses on related topics, e.g. Computer Vision, 3-D Computer Vision, Graphics, Artificial Intelligence, etc.
The book is mainly based on my lecture notes for several undergraduate and graduate classes I have offered over the past several years, while a number of topics stem from my research publications co-authored with my students. This book takes into account the needs of learners with various professional backgrounds, as well as those of self-learners. Furthermore, it can be used as a reference guide for practitioners and professionals in related fields.
To aid in comprehension, the book includes a wealth of self-test questions (with hints and answers). On the one hand, these questions help teachers to carry out online teaching and interact with students during lectures; on the other, self-learners can use them to assess whether they have grasped the key content.
Table of ContentsChapter 1 Introduction
1.1 Human Vision and Characteristics
1.2 Computer Vision Theory and Model
1.3 3D Vision System and Image Technology
1.4 Book Overview
Chapter 2 Camera Calibration
2.1 Linear Camera Model
2.2 Non-Linear Camera Model
2.3 Traditional Calibration Methods
2.4 Self-Calibration Methods
Chapter 3 3D Image Acquisition
3.1 High-Dimensional Image
3.2 Depth Image
3.3 Direct Depth Imaging
3.4 Stereo Vision Imaging
Chapter 4 Video and Motion Information
4.1 Video Basic
4.2 Motion Classification and Representation
4.3 Motion Information Detection
4.4 Motion-Based Filtering
Chapter 5 Moving Object Detection and Tracking
5.1 Differential Image
5.2 Background Modeling
5.3 Optical Flow Field and Motion
5.4 Moving Object Tracking
Chapter 6 Binocular Stereo Vision
6.1 Stereo Vision Process and Modules
6.2 Region-Based Stereo Matching
6.3 Feature-Based Stereo Matching
6.4 Error Detection and Correction of Parallax Map
Chapter 7 Monocular Multiple Image Reconstruction
7.1 Photometric Stereo
7.2 Shape from Illumination
7.3 Optical Flow Equation
7.4 Shape from Motion
Chapter 8 Monocular Single Image Reconstruction
8.1 Shape from Shading
8.2 Solving Brightness Equation
8.3 Shape from Texture
8.4 Detection of Texture Vanishing Points
Chapter 9 3-D Scene Representation
9.1 Local Features of the Surface
9.2 3-D Surface Representation
9.3 Construction and Representation of Iso-Surfaces
9.4 Interpolate 3-D Surfaces from Parallel Contours
9.5 3-D Entity Representation
Chapter 10 Scene Matching
10.1 Matching Overview
10.2 Object Matching
10.3 Dynamic Pattern Matching
10.4 Graph Theory and Graph Matching
10.5 Line Drawing Signature and Matching
Chapter 11 Knowledge and Scene Interpretation
11.1 Scene Knowledge
11.2 Logic System
11.3 Fuzzy Reasoning
11.4 Scene Classification
Chapter 12 Spatial-Temporal Behavior Understanding
12.1 Spatial-Temporal Technology
12.2 Spatial-Temporal Interest Point Detection
12.3 Spatial-Temporal Dynamic Trajectory Learning and Analysis
12.4 Spatial-Temporal Action Classification and Recognition
Appendix A Visual Perception
A.1 Shape Perception
A.2 Spatial Perception
A.3 Motion Perception
Self-Test Questions Answers to Self-Test Questions Bibliography Subject Index
