EECS 504: Foundations of Computer Vision
| Instructor: | Jason Corso (jjcorso) |
| GSI : | Parker Koch (pakoch) |
| Lecture Time/Place: | MW 1200-1330 in 1500 EECS |
| Discussion Time/Place: | W 1900-2000 in Cooley G906 Discussion is optional |
| Prof. Office Hours: | T 1350-1450 and R 1100-1200 by appt. |
| GSI Office Hours (in EECS 2420): | TR 1430-1600 |
| Syllabus: | /~jjcorso/t/504F16/files/504F16_syllabus.pdf |
Course Description
Computer Vision seeks to extract useful information from images of various types. This course covers the foundations of computer vision. It emphasizes computer vision as a search for visual invariants and computer vision as mathematical modeling. Foundational representations of images and image content will be discussed. Cross-cutting problems of reduction (e.g., feature extraction, segmentation), estimation (e.g., post estimation, camera calibration), and matching (e.g., image stitching, stereo reconstruction) will be concretely defined and elaborated through many real examples from modern computer vision.
The course is designed for graduate students and will prepare them for future work in computer vision. The students will develop a strong understanding of formulating and solving problems in computer vision. The students will develop a rich, capable computer vision system over the course of the semester through homework assignments.
Rough Topic Outline
- Representation and Computer Vision as Optimization
- Visual Invariance (photometric, geometric (shift, rotation, affine, scale), projective, structural, deformation, rate)
- Reduction Problems (feature extraction, segmentation and grouping, etc.)
- Estimation Problems (line and curve fitting, pose estimation, camera calibration, bundle adjustment, etc.)
- Matching Problems (stereo correspondence, image stitching, image registration, etc.)
