UsefulLinks
Computer Science
Computer Vision
Computer Vision and Image Analysis
1. Foundations of Computer Vision
2. Image Formation and Representation
3. Fundamental Image Processing
4. Feature Detection and Description
5. Image Segmentation
6. Classical Object Recognition
7. Deep Learning for Computer Vision
8. Motion and Video Analysis
9. 3D Computer Vision
10. Computer Vision Applications
8.
Motion and Video Analysis
8.1.
Video Representation
8.1.1.
Temporal Dimension
8.1.1.1.
Frame Sequences
8.1.1.2.
Frame Rate Considerations
8.1.1.3.
Temporal Sampling
8.1.2.
Video Formats
8.1.2.1.
Compression Standards
8.1.2.2.
Color Spaces for Video
8.1.2.3.
Interlaced vs. Progressive
8.1.3.
Spatiotemporal Features
8.1.3.1.
3D Structure
8.1.3.2.
Motion Patterns
8.1.3.3.
Temporal Consistency
8.2.
Motion Estimation
8.2.1.
Optical Flow
8.2.1.1.
Flow Field Definition
8.2.1.2.
Brightness Constancy Assumption
8.2.1.3.
Aperture Problem
8.2.2.
Dense Optical Flow
8.2.2.1.
Horn-Schunck Method
8.2.2.2.
Lucas-Kanade Method
8.2.2.3.
Variational Methods
8.2.3.
Sparse Optical Flow
8.2.3.1.
Feature-based Tracking
8.2.3.2.
KLT Tracker
8.2.3.3.
Pyramidal Implementation
8.2.4.
Block Matching
8.2.4.1.
Template Matching
8.2.4.2.
Search Strategies
8.2.4.3.
Motion Vectors
8.3.
Object Tracking
8.3.1.
Single Object Tracking
8.3.1.1.
Template-based Tracking
8.3.1.2.
Correlation Filters
8.3.1.3.
Mean Shift Tracking
8.3.2.
Probabilistic Tracking
8.3.2.1.
Kalman Filters
8.3.2.2.
Particle Filters
8.3.2.3.
State Space Models
8.3.3.
Multiple Object Tracking
8.3.3.1.
Data Association
8.3.3.2.
Track Management
8.3.3.3.
Occlusion Handling
8.3.4.
Deep Learning Tracking
8.3.4.1.
Siamese Networks
8.3.4.2.
Correlation Filter Networks
8.3.4.3.
Transformer-based Trackers
8.4.
Action Recognition
8.4.1.
Handcrafted Features
8.4.1.1.
Motion History Images
8.4.1.2.
Optical Flow Features
8.4.1.3.
Trajectory Features
8.4.2.
Deep Learning Approaches
8.4.2.1.
3D CNNs
8.4.2.2.
Two-stream Networks
8.4.2.3.
Temporal Segment Networks
8.4.3.
Sequence Modeling
8.4.3.1.
Recurrent Neural Networks
8.4.3.2.
LSTM Networks
8.4.3.3.
Attention Mechanisms
8.4.4.
Spatiotemporal Modeling
8.4.4.1.
Space-time Interest Points
8.4.4.2.
Dense Trajectories
8.4.4.3.
Pose-based Recognition
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9. 3D Computer Vision