Overview of computer vision problems and techniques for analyzing the content of images and video. Topics include image formation, edge detection, image segmentation, pattern recognition, texture analysis, optical flow, stereo vision, shape representation and recovery techniques, issues in object recognition, and case studies of practical vision systems.
4
UnitsLetter
Grading1, 2, 3
PasstimeUpper division only
Level LimitEngineering
CollegeLectures are solid. Homeworks are key to understanding course concepts. Exams are tough (particularly the midterm), but not unfair. Answers questions frequently on Piazza. An ideal professor.
Really good professor. Tests were very fair, make sure you do the homework properly and adequately understand the concepts.
Class had 3 programming assignments with EC and optional HW assignments. Programing assignments were pretty useless but HW is necessary because they're just like the exams. Exams are super long and focus on very minor details so you must know everything. Prof is really nice and lectures were interesting and easy to follow. Super organized class.
This class had multiple choice exams and the programming assignments were easy, and lowest scores are dropped. You get to learn introductory computer vision topics and it is a fun experience overall.
Don't see how anyone could have any complaints about this guy.
A fantastic professor who explains the requirements well. He is knowledgeable in his field and helps students understand and explore outside of lectures. The class is graded mainly on projects and tests. Homework is optional in this class.
Maryam Majedi
2.6
Elizabeth Belding
4.0
Prabhanjan Ananth
3.6
Tobias Hollerer
4.5
Nabeel Nasir
4.9
Chandra Krintz
4.0