Prof. Dr. Paolo Favaro
Mr. Adrian Wälchli
Mr. Adam Bielski
Mr. Llukman Çerkezi
Mr. Sepehr Sameni
|Location||Seminarraum 002, Engehaldenstrasse 8|
|Time||Tuesdays 14.15-16.00 (lecture), and 16.15-17.00 (exercises)|
|Exam||February 16 2021 from 10:00 to 12:00 at Seminarraum 002, Engehaldenstrasse 8|
*** FORM OF IMPLEMENTATION (COVID19 MEASURES) ***
We have decided that we will offer the course only online.
The lectures and tutorials will be pre-recorded and posted as podcasts
one week in advance. During the lecture time we will hold
instead a Q&A session for both the lectures and the tutorials.
For discussions beside the classroom, we adopt piazza.com.
The link will be provided in the slides of the introductory class
and made available in ILIAS.
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 Python 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 Python. 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|