| Course |
CSE327 |
| Title |
Fundamentals of Computer Vision |
| Credits |
3 |
| Course Coordinator |
Dimitris Samaras
|
| Description |
Introduces fundamental concepts, algorithms, and techniques in visual information processing. Covers image formation, binary image processing, image features, model fitting, optics, illumination, texture, motion, segmentation, and object recognition.
Bulletin Link
|
| Prerequisite |
CSE 214 or CSE 230 or CSE 260; AMS 210 or MAT 211; CSE or ISE major
|
| Course Outcomes |
- Working knowledge of fundamental concepts and computational techniques in visual information processing.
- An ability to design algorithms for understanding images and video, such as segmentation, edge detection, and reflectance analysis.
|
| Textbook |
Computer Vision: Algorithms & Applications by Richard Szeliski; Springer (ISBN # 978-1848829343)
|
| Major Topics Covered in Course |
- Computational algorithms and their implementation for the following applications: automatic inspection and measurement based on binary image processing (two-dimensional machine vision).
- Gray-level image processing and analysis techniques (e.g. image segmentation, edge detection, image filtering, curve fitting).
- Three-dimensional shape recovery through stereo image analysis.
- Object recognition based on feature vector classification and template matching.
|
| Laboratory Projects |
- Image processing: mask-based filtering, median filtering, subsampling, edge-detection (2 weeks)
- Feature detection, Morphing and Mosaicing images (2 weeks)
- Stereo Reconstruction (2 weeks)
- Final project. Select from suggested topcs (4 weeks)
|
| Course Webpage |
CSE327
|
