EECS 504: Foundations of Computer Vision
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
Other Pieces of InformationThe course is designed for graduate students and will prepare them for future work in computer vision. No prior computer vision course is assumed. The course will cater broadly to students across the College of Engineering, LSA (Stats, Math) and School of Information.
ECE Program Requirement InformationThis course satisfies the ECE M.S. Computer Vision major requirement of a graduate computer vision course.
CSE Program Requirement InformationThis course satisfies the CSE M.S. degree requirement of "at least 15 credit hours of CSE technical courses at the 500 level or above". This course counts as a depth course for the CSE PhD requirements.