🙋♀️ Syllabus for Fall 2025 🙌
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 2025 Schedule 
| Weeks | Tuesday | Thursday |
|---|---|---|
|
Week 01 |
8/26 Lec 01: History, Overview |
8/28 Lec 02: Camera Model |
|
Week 02 |
9/02 Lec 03: Coloring and Warping |
9/04 Lec 04: Filtering |
|
Week 03 |
9/9 Lec 05: Morphology |
9/11 Lec 06: Edge Detection |
|
Week 04 |
9/16 Lec 07: Pyramids & Histogram |
9/18 Lec 08: Feature |
|
Week 05 |
9/23 Lec 09: Optical Flow |
9/25 No class; Rest |
|
Week 06 |
9/30 Lec 10: Hough Transform |
10/02 Lec 11: Alignment and Fitting |
|
Week 07 |
10/07 Lec 12: Stitching & RANSAC |
10/9 Lec 13: Epipolar Geometry |
|
Week 08 |
10/14 FALL BREAK |
10/16 Tutorials on Homework/Projects |
|
Week 09 |
10/21 Lec 14: Stereo Vision |
10/23 Lec 15: Texture |
|
Week 10 |
10/28 Lec 16: Segmentation |
10/30 Lec 17: Classification |
|
Week 11 |
11/04 Lec 18: Recognition |
11/06 Lec 19: Detection |
|
Week 12 |
11/11 Lec 20: Face Detection |
11/13 Lec 21: Instance Retrieval |
|
Week 13 |
11/18 Lec 22: Multi-layer Perceptron |
11/20 Lec 23: Deep Learning |
|
Week 14 |
11/25 Lec 24: Object Detection |
11/27 THANKSGIVING BREAK |
|
Week 15 |
12/02 L25: Image Generation |
12/04 L26: Multi-task Learning |
|
Week 16 |
12/9 No class (UB reading day) |
12/11 No Class |
|
Week 17 |
12/16 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.
