Prof. Dr. Paolo Favaro
Mr. Adrian Wälchli
Ms. Qiyang Hu
Mr. Xiaochen Wang
Mr. Givi Meishvili
|Location||Seminarraum 002, Engehaldenstrasse 8|
|Time||Tuesdays 14.15-16.00 (lecture), and 16.15-17.00 (exercises)|
|Exam||12th of February 2019 from 10:00 to 12:00 at Seminarraum 002, Engehaldenstrasse 8|
This course covers fundamental topics in computer vision. The course will provide an introduction to image formation, image processing, feature detection, segmentation, multiple view geometry and 3D reconstruction, and motion.
Upon successful completion of this class, you will be able to:
The course requires students to be familiar with the basics of linear algebra, probability theory and MATLAB programming. A brief review of these subjects will be carried out during the exercise sessions.
The following books are recommended as additional reading:
The exercises are a prerequisite for registering for the exam. There will be several homework assignments and the deadlines will be specified by the teaching assistant. For admission to the examination one must pass every assignment. The assignments will also contribute to 30% of the final mark. The description of each assignment will be made available in ILIAS. Notice that many assignments will require use of Matlab. All the homework assignments are also intended for exam preparation.
The following table provides an overview of the content of the lectures during the semester. Please check it periodically as it might be updated.
|1||Introduction & projection models||Forsyth & Ponce Ch 1|
|2||Camera models & Linear filters & edges||Forsyth & Ponce Ch 1, 2, 7, 8|
|3||Energy minimization methods|
|4||Photometric stereo & shading||Forsyth & Ponce Ch 5|
|5||Tracking & Optical flow||Forsyth & Ponce Ch 17|
|6||Interest points detection|
|7||Registration & Fitting||Forsyth & Ponce Ch 15, 17|
|8||Epipolar geometry & Stereo||Forsyth & Ponce Ch 10, 15|
|9||Multiview stereo & Structure from motion||Forsyth & Ponce Ch 11, 12, 13|
|10||Recognition & machine learning||Grauman & Leibe|
|11||Bayesian methods||Forsyth & Ponce Ch 16, C. Bishop, S. Prince|