Image Processing and Computer Vision

Lecturer: Prof. Dr. Joachim Weickert

Assistant: Karl Schrader
 

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

Online lectures based on the Zoom platform: (privacy information):
Tuesday, 10:15-12:00
Friday, 10:15-12:00

First lecture: Tuesday, April 16
The Zoom link can be found in the Materials section.

Tutorials: 2 hours each week; see below.

Based on the maximum capacity of our tutorials, this lecture was temporarily limited to 175 participants.
As many students unfortunately did not participate in the tutorials despite registering, we have some spare capacity. Accordingly, the registration has been reopened without a limit.

Type of Lectures – Prerequisites – Tutorials – Registration – Written Exams – Contents – Literature

Type of Lectures

Prerequisites

Tutorials

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. You can earn up to 2 bonus points in a tutorial by presenting a solution to a classroom assignment. 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 permitted, but all persons must be in the same tutorial group.

If you miss a tutorial because you are sick, you can still get the points for participation, if you bring a doctor's certificate.

If you have questions concerning the tutorials, please do not hesitate to contact Karl Schrader.

Seven groups are scheduled for Tuesday and Wednesday:

• Group T1: Tuesday, 12:15-14:00
  Tutor: Aiman Al-Azazi
  Building E1.3, Seminar Room 014
• Group T2: Tuesday, 14:15-16:00
  Tutor: Aiman Al-Azazi
  Building E1.3, Seminar Room 014
• Group T3: Tuesday, 16:15-18:00
  Tutor: Karl Schrader
  Building E1.3, Seminar Room 014
• Group W1: Wednesday, 10:15-12:00
  Tutor: Karl Schrader
  Building E1.1, Seminar Room 106
• Group W2: Wednesday, 12:15-14:00
  Tutor: Vipil Vijay
  Building E2.5, Seminar Room 4 (U.16)
• Group W3: Wednesday, 14:15-16:00
  Tutor: Vipil Vijay
  Building E2.5, Seminar Room 4 (U.16)
• Group W4: Wednesday, 16:15-18:00
  Tutor: Lianjia Liu
  Building E2.5, Seminar Room 4 (U.16)

If you have difficulties with the programming assignments, feel free to participate in the Optional Guided Programming each Tuesday, 18:15-20:00, in the bioinformatics CIP pool in building E 2.1 room 003.

Registration

Written Exams

There will be two written exams, one at the beginning and one at the end of the semester break.

The first written exam takes place on Friday, August 9, 14:00 - 17:00, in GHH (E2.2).

The second written exam takes place on Friday, September 27, 14:00 - 17:00, in building E2.5, lecture halls I and II.

Please be at the exam hall at 13:30 to allow for sufficient time for all organisational matters to be handled.

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, but each exam will count as an attempt individually. Exam admissions from previous semesters do not qualify you to take part in the exams of this course. 

Both exams will be closed book exams. You will have to follow these rules:

• You are allowed and obliged to bring three things to your desk only: Your student ID card (Studierendenausweis), a ball-pen that has no function other than writing, and a so-called cheat sheet. This cheat sheet is a A4 page with formulas or important equations from the lecture. Please note that the cheat sheet has to be handwritten by yourself. It will be collected at the end of the exam, and you can get it back at the exam inspection.
• Everything else has to be deposited at the walls of the exam hall. In particular, electronic devices (including your cell phone), bags, jackets, briefcases, lecture notes, homework and classroom work solutions, additional handwritten notes, books, dictionaries, and paper are not allowed at your desk.
• Please keep your student ID card ready for an attendance check during the exam.
• Do not use pencils or pens that are erasable with a normal rubber.
• You are not allowed to take anything with you that contains information about the exam.
  A violation of this rule means failing the IPCV course.
• You must stay until the exam is completely over.

If a student is unable to attend the written exams due to reasons beyond his/her control (e.g. because of an illness (medical certificate required immediately), or another exam at the same day), we aim to provide alternative options such as an online oral exam.

Contents

Course material is available on this webpage in order to support the teaching and the tutorials, not to replace them. Additional organisational information, examples and explanations that may be relevant for your understanding and the exam are provided in the lectures and tutorials. It is solely your responsibility - not ours - to make sure that you receive this infomation. Here is a preliminary list of the planned contents:

PART I: FOUNDATIONS AND TRANSFORMATIONS

PART II: IMAGE PROCESSING

PART III: COMPUTER VISION AND IMAGE UNDERSTANDING

Literature

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.