COURSE SCHEDULE
The table below outlines a tentative schedule for this course over a 13-week term. In conjunction to the lectures there will be a 2-hour weekly lab session intended to give practical demonstration of the computer vision principles presented in the lectures and to provide experience in using OpenCV for the development of computer vision components and systems.
Ackowledgements: The slides are a combination of multiple resources and materials generously made publicly available by L. Shapiro, J. Hays, S. Lazebnik, D. Forsyth, J. Ponce, J. Koenderink, S. Seitz, R. Szeliski, B. Freeman, M. Pollefeys, D. Lowe, K. Grauman, A. Efros, F. Durand, L. Fei-Fei, A. Torralba, R. Fergus, F-F. Li, A. Karpathy, J. Johnson. In particular the material is heavily based on Drs L. Shapiro's and J. Hays' slides.
| Date | Topics | Reading | Slides | Comments |
|---|---|---|---|---|
| 1 > | Syllabus Introduction to Computer VisionDigitization (sampling, quantization), Images | Szeliski Ch. 1, 3.2, Forsyth/Ponce Ch. 4 | pdf pdf pdf | |
| 2 > | Image Operations (filtering) Edge detection | Szeliski Ch. 3.2, 3.4, 3.5, 4.2 | pdf pdf | Cipolla & Gee on edge detection Assignment 1 out |
| 3 > | Geometric transformations Interest points | Szeliski Ch.4.1.1 | pdf pdf | Harris paper |
| 4 > | Feature Descriptors Image Stitching I | Szeliski Ch.4.1.2-4.1.3Szeliski Ch.6.1 | pdf pdf | Assignment 1 due Assignment 2 out SIFT paper |
| 5 > | Image Stitching II Cameras | Szeliski Ch.6 Szeliski Ch.2 | pdf pdf | |
| 6 > | Quiz #1 | |||
| 7 > | Multiple Views (stereo, epipolar geometry) | Szeliski Ch. 9 | pdf pdf | Assignment 2 due Project out |
| 8 > | Multiple views (Structure from Motion, Multi-View Stereo ) | Szeliski Ch. 7 Szeliski Ch. 8.4 | pdf pdf | |
| 9 > | Motion and Optical Flow Image classification (k-NN, cross-validation, hyperparameters) | Szeliski Ch.14.1, 14.2 | pdf pdf | |
| 10 > | Image Classification (Linear classifiers) Loss Function and Optimization | |||
| 11 > | Convolutional Neural Networks Back-propagation and Neural Networks | Project due | ||
| 12 > | Quiz #2 | |||
| 13 > | Convolutional Neural Networks - Case studies | Project evaluation |