Syllabus

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