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- Introduction - Image Processing / Computer Vision
- Spatial/ Frequency Domain Processing
- Background Modeling
- Local Features (Harris/SIFT/KB/STIP)
- Object Detection and Recognition (Eigen Faces, Sparse Representation)
- Face Detection and Recognition
- Segmentation (Unsupervised: Watershed, Levelset, Active Contour, GraphCut)
- Segmentation (Supervised: Agglomerative clustering, Segmentation as pixel classification - UNets, FCN)
- MS Theory
- MS Tracking
- Kalman, Particle Filter based tracking
- Multi-target/Multi-camera tracking
- Motion Estimation, Optical Flow
- Action Recognition
- Stereo and MonoDepth Estimation
- Decision Trees/Random Forest
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