🙋♀️ Syllabus for Fall 2024 🙌
This course is an introduction to those areas of Artificial Intelligence that deal with fundamental issues and techniques of computer vision and image processing. The emphasis is on physical, mathematical, and information-processing aspects of the vision. Topics to be covered include image formation, edge detection and segmentation, convolution, image enhancement techniques, extraction of features such as color, texture, and shape, object detection, 3-D vision, and computer vision system architectures and applications. Together, we will explore fascinating topics related to Computer Vision and Image Processing, including Optical Image Formation, Feature Extraction, Classification, and Recognition.
Instructor
| Name | Title | |
|---|---|---|
 |
Assistant Professor |
Fall 2024 Schedule 
The Schedule below is tentative and will be updated shortly.
| Weeks | Tuesday | Thursday |
|---|---|---|
|
Week 01 |
8/27 Lec 01: History, Overview |
8/29 Lec 02: Camera Model |
|
Week 02 |
9/03 Lec 03: Coloring and Warping |
9/05 Lec 04: Filtering |
|
Week 03 |
9/10 Lec 05: Morphology |
9/12 Lec 06: Edge Detection |
|
Week 04 |
9/17 Lec 07: Pyramids & Histogram |
9/19 Lec 08: Feature |
|
Week 05 |
9/24 Lec 09: Optical Flow |
9/26 Lec 10: Hough Transform |
|
Week 06 |
10/01 Lec 11: Alignment and Fitting |
10/03 Lec 12: Stitching & RANSAC |
|
Week 07 |
10/08 Lec 13: Epipolar Geometry |
10/10 Lec 14: Stereo Vision |
|
Week 08 |
10/14 - 10/15 FALL BREAK |
10/17 Lec 15: Texture |
|
Week 09 |
10/22 Lec 16: Segmentation |
10/24 Lec 17: Classification |
|
Week 10 |
10/29 Lec 18:Recognition |
10/31 Lec 19:Detection |
|
Week 11 |
11/05 Lec 20:Face Detection |
11/07 Lec 21:Instance Retrieval |
|
Week 12 |
11/12 Lec 22:Multi-layer Perceptron |
11/14 Lec 23:Deep Learning |
|
Week 13 |
11/19 Modern CV Topics I |
11/21 Modern CV Topics II |
|
Week 14 |
11/26 Modern CV Topics III |
11/27 - 11/30 THANKSGIVING BREAK |
|
Week 15 |
12/03 Modern CV Topics IV |
12/05 Final Exam Recap |
|
Week 16 |
12/10 No class (UB reading day) |
12/12 Final Exam |
Projects
Homeworks
Acknowledgement
Many materials are derived from Prof. David Doermann’s course slides. We give special thanks to Prof. Junsong Yuan and Prof. Nalini Ratha for providing fruitful suggestions. We also thank numerous generous researchers for contributing to the contents, which include but are not limited to K. Kitani, K. Grauman, S. Seitz, S. Marschner, M. Hebert, Fei-Fei Li, L. Lazebnik, R. Szeliski, A. Efros, A. Oliva, B. Leibe, D. Hoiem, A. Moore, and D. Lowe, etc.
