Welcome to the homepage of the lecture

Image Processing and Computer Vision

Summer Term 2023

Image Processing and Computer Vision

Three Teaching Awards (2 in Computer Science, 1 in Mathematics)

Lecturer: Dr. Pascal Peter
Office hour: TBD

Coordinator of tutorials: Michael Ertel
Office hour: TBD

Summer Term 2023

Lectures (4h) with theoretical and programming assignments (2h);
(9 ETCS points)

Lectures:
Tuesday, 10:15-12:00, Building E2.2, Günter Hotz Lecture Theatre and online
Friday, 10:15-12:00, Building E1.3, HS001 and online

First lecture: Friday, April 14

News – Type of Lectures – Prerequisites – Tutorials – Registration – Written Exams – Lecture notes / Assignments – Literature

03.03.2023: Website is now online. Registration and more detail information will be available closer to the start of the semester.

This class gives a broad introduction to the mathematically well-founded and model-based areas of image processing and computer vision. These fields are important in numerous applications including medical image analysis, computer-aided quality control, robotics, computer graphics, multimedia, data science, machine learning, and artificial intelligence. The class is required for starting a bachelor thesis in our group.

It is planned that this class will be continued in the winter term with the class "Differential Equations in Image Processing and Computer Vision" which will bring you closer to our research topics. Both classes are required to pursue a master thesis in our group.

This course is suitable for students of Visual Computing, Mathematics, Computer Science, Mathematics and Computer Science, Data Science and Artificial Intelligence, Bioinformatics, Mechatronics, and Physics. It counts e.g. as a visual computing core area course within the Visual Computing program, and as a core course (Stammvorlesung) within Mathematics or Computer Science.

It is based on undergraduate mathematical knowledge from the first three semesters (such as "Mathematics for Computer Scientists I-III"). For the programming assignments, some elementary knowledge of C is required. The lectures are given in English.

The tutorials are planned to take place as face-to-face teaching with online options. Further information will be announced closer to the beginning of the semester.

The tutorials include homework assignments (theory and programming) as well as classroom assignments. The programming assignments give an intuition about the way how image processing and computer vision algorithms work, while the theoretical assigments provide additional mathematical insights. Classroom assignments are supposed to be easier and should guide you gently to the main themes.

For the homework assignments you can obtain up to 24 points per week. Actively participating in the classroom assignments gives you 12 more points per week, regardless of the correctness of your solutions. To qualify for both exams you need 2/3 of all possible points. For 13 weeks, this comes down to 13 x 24 = 312 points. Working in groups of up to 3 people is highly encouraged.

If you have questions concerning the tutorials, please do not hesitate to contact Michael Ertel.

The tutorials are planned to be partially in person and online, and are scheduled for Tuesday and Wednesday:

• Group T1: Tuesday, 12:15-14:00
  Building E1.3, Seminar Room 014 or online
  


• Group T2: Tuesday, 14:15-16:00
  Building E1.3, Seminar Room 015 or online
  


• Group T3: Tuesday, 16:15-18:00
  Building E1.3, Seminar Room 015 or online
  


• Group W1: Wednesday, 10:15-12:00
  Building E1.1, Seminar Room 106 or online
  


• Group W2: Wednesday, 12:15-14:00
  Building E2.5, Seminar Room 3(U.11) or online
  


• Group W3: Wednesday, 14:15-16:00
  Building E2.5, Seminar Room 3(U.11) or online
  


• Group W4: Wednesday, 16:15-18:00
  Building E2.5, Seminar Room 3(U.11) or online
  

If you have difficulties with the programming assignments, feel free to consult our

• Optional Guided Programming (OGP) Helpdesk: Tuesday, 18:15-20:00
  Please send e-mail for online appointments to Michael Ertel.

At the beginning of the semester, you will have to register for this course.

Please do not forget to register also in the HISPOS/LSF system (apart from Erasmus students). This system administrates your exam admission and your grades. It will allow registrations in the end of April.

The first exam takes place on July 25, 14:00-17:00.
The second exam takes place on October 9, 14:00-17:00.

In order to qualify for the exams you need a total amount of 2/3 of all possible points from the homework and classroom assignments. In case of qualification, you are allowed to take part in both exams. The better grade counts. Each exam counts as an individual attempt.

Both exams will be closed book exams. Detailed rules can be found on MS Teams.

Lecture content in form slides and assignments are available for download via Microsoft Teams. Access will be granted after registration. In addition, we will provide pre-recoreded lecture videos from previous semesters. Note that the initial registration requires manual confirmation and can thus be delayed a bit.

There is no specific text book for this class, but many of our image processing topics are covered in one of the following books:

• J. Bigun: Vision with Direction. Springer, Berlin, 2010.
• R. C. Gonzalez, R. E. Woods: Digital Image Processing. Addison-Wesley, International Edition, 2017.
• K. D. Tönnies: Grundlagen der Bildverarbeitung. Pearson Studium, München, 2005.

Computer vision books include

• R. Klette: Concise Computer Vision. Springer, London, 2014.
• R. Szeliski: Computer Vision: Algorithms and Applications. Springer, New York, Second Edition, 2022.
• E. Trucco, A. Verri: Introductory Techniques for 3-D Computer Vision. Prentice Hill, Upper Saddle River, 1998.

These and further books can be found in the mathematics and computer science library.
Furthermore, there is an interesting online compendium, where many researchers have written survey articles.
If you are looking for a specific reference, check out the Annotated Computer Vision Bibliography.
Many highly cited articles can be found via the Google Scholar webpage.

MIA Group
©2001-2023
The author is not
responsible for
the content of
external pages.

Imprint - Data protection