Fundamentals of Computer Vision
Fundamentals of Computer Vision
A gentle, accessible introduction to foundational concepts in computer vision and computational perception.
About the Book
Though initially intended as a companion guide to graduate courses at Georgia Tech, it has grown into a full-blown, 300-page behemoth designed to be an accessible introduction to foundational concepts in computer vision. It features cross-references, thorough explanations, and external links to minimize assumed knowledge from the reader. From facial recognition to augmented reality, many of the algorithms and ideas presented are still widely used today and form the backbone to computational perception.
(This work was donation-based while I was authoring it; if you were one of the wonderful folks who fed my caffeine addiction and would like a copy of the latest edition, contact me!)
About the Author
Table of Contents
- Preface
- Introduction
-
2.
Basic Image Manipulation
- 2.1. Images as Functions
- 2.2. Image Filtering
- 2.3. Linearity and Convolution
- 2.4. Boundary Issues
- 2.5. More Filter Examples
- 2.6. Filters as Templates
- 3.
Edge Detection
- 3.1. The Importance of Edges
- 3.2. Gradient Operator
- 3.3. Dimension Extension Detection
- 3.4. From Gradients to Edges
- 4. Hough Transform
- 4.1. Line Fitting
- 4.2. Finding Circles
- 4.3. Generalization
- 5.
Frequency Analysis
- 5.1. Basis Sets
- 5.2. Fourier Transform
- 5.3. Aliasing
- 6.
Blending
- 6.1. Crossfading and Feathering
- 6.2. Image Pyramids
- 6.3. Pyramid Blending
- 6.4. Poisson Blending
- 7.
Cameras and Images
- 7.1. Cameras
- 7.2. High Dynamic Range
- 7.3. Perspective Imaging
- 7.4. Stereo Geometry
- 7.5. Extrinsic Camera Parameters
- 7.6. Intrinsic Camera Parameters
- 7.7. Total Camera Calibration
- 7.8. Calibrating Cameras
- 7.9. Using the Calibration
- 7.10. Calibrating Cameras: Redux
- 8.
Multiple Views
- 8.1. Image-to-Image Projections
- 8.2. The Power of Homographies
- 8.3. Projective Geometry
- 8.4. Applying Projective Geometry
- 8.5. Summary
- 9.
Feature Recognition
- 9.1. Finding Interest Points
- 9.2. Matching Interest Points
- 9.3. Coming Full Circle: Feature-Based Alignment
- 9.4. Conclusion
- 10.
Photometry & Color Theory
- 10.1. Photometry
- 10.2. Color Theory
- 11.
Motion
- 11.1. Motion Estimation
- 11.2. Motion Models
- 12.
Tracking
- 12.1. Modeling Dynamics
- 12.2. Kalman Filter
- 12.3. Particle Filters
- 12.4. Real Tracking
- 12.5. Mean-Shift
- 12.6. Issues in Tracking
- 13.
Recognition
- 13.1. Generative Supervised Classification
- 13.2. Principal Component Analysis
- 13.3. Incremental Visual Learning
- 13.4. Discriminative Supervised Classification
- 13.5. Support Vector Machines
- 13.6. Visual Bags of Words
- 14.
Video Analysis
- 14.1. Feature Tracking & Registration
- 14.2. Video Textures
- 14.3. Background Subtraction
- 14.4. Cameras in Motion
- 14.5. Activity Recognition
- 15.
Segmentation
- 15.1. Clustering
- 15.2. Mean-Shift, Revisited
- 15.3. Texture Features
- 15.4. Graph Cuts
- 16.
Binary Image Analysis
- 16.1. Thresholding
- 16.2. Connected Components
- 16.3. Morphology
- 17.
Depth and 3D Sensing
- 17.1. Structure from Motion
- 17.2. 3D Sensing
- Appendix A. Linear Algebra Primer
- Appendix B. Human Vision System
- Index of Terms
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