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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
Motion and Video Analysis
Video Representation
Temporal Dimension
Frame Sequences
Frame Rate Considerations
Temporal Sampling
Video Formats
Compression Standards
Color Spaces for Video
Interlaced vs. Progressive
Spatiotemporal Features
3D Structure
Motion Patterns
Temporal Consistency
Motion Estimation
Optical Flow
Flow Field Definition
Brightness Constancy Assumption
Aperture Problem
Dense Optical Flow
Horn-Schunck Method
Lucas-Kanade Method
Variational Methods
Sparse Optical Flow
Feature-based Tracking
KLT Tracker
Pyramidal Implementation
Block Matching
Template Matching
Search Strategies
Motion Vectors
Object Tracking
Single Object Tracking
Template-based Tracking
Correlation Filters
Mean Shift Tracking
Probabilistic Tracking
Kalman Filters
Particle Filters
State Space Models
Multiple Object Tracking
Data Association
Track Management
Occlusion Handling
Deep Learning Tracking
Siamese Networks
Correlation Filter Networks
Transformer-based Trackers
Action Recognition
Handcrafted Features
Motion History Images
Optical Flow Features
Trajectory Features
Deep Learning Approaches
3D CNNs
Two-stream Networks
Temporal Segment Networks
Sequence Modeling
Recurrent Neural Networks
LSTM Networks
Attention Mechanisms
Spatiotemporal Modeling
Space-time Interest Points
Dense Trajectories
Pose-based Recognition
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9. 3D Computer Vision