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Image Processing and Computer Vision

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

 

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 is limited to 175 participants.
We are unfortunately unable to implement a list of substitutes due to administrative reasons.


Type of LecturesPrerequisitesTutorialsRegistrationWritten ExamsContentsLiterature


Type of Lectures

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 by 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.
 

Prerequisites

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.
 

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
    Building E1.3, Seminar Room 014
  • Group T2: Tuesday, 14:15-16:00
    Building E1.3, Seminar Room 014
  • Group T3: Tuesday, 16:15-18:00
    Building E1.3, Seminar Room 014
  • Group W1: Wednesday, 10:15-12:00
    Building E1.1, Seminar Room 106
  • Group W2: Wednesday, 12:15-14:00
    Building E2.5, Seminar Room 4 (U.16)
  • Group W3: Wednesday, 14:15-16:00
    Building E2.5, Seminar Room 4 (U.16)
  • Group W4: Wednesday, 16:15-18:00
    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

You have register for this course and enter your tutorial preferences via the CMS until 19.4.2024 23:59.
Please do not forget to register for the exam also in the HISPOS/LSF system (apart from Erasmus students). This system administrates your exam admission and your grades. It will allow registrations starting by the end of April.
 

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.

The second written exam takes place on Friday, September 27, 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, 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

Date Topic
16.04. Foundations I: Definitions, Image Types, Discretisation
19.04. Foundations II: Degradations in Digital Images
23.04. Foundations III: Colour Perception and Colour Spaces
26.04. Image Transformations I: Continuous Fourier Transform
30.04. Image Transformations II: Sampling Theorem and Discrete Fourier Transform
03.04. Image Transformations III: Discrete Cosine Transform and Image Pyramids
07.05. Image Transformations IV: Discrete Wavelet Transform
10.05. Image Compression
14.05. Image Interpolation

 

PART II: IMAGE PROCESSING

Date Topic
17.05. Point Operations
21.05. Linear Filters I: System Theory
24.05. Linear Filters II: Derivative Filters
28.05. Linear Filters III: Detection of Edges and Corners
31.05. Nonlinear Filters I: Morphology and Median Filters
04.06. Nonlinear Filters II: Wavelet Shrinkage, Bilateral Filters, NL-Means
07.06. Nonlinear Filters III: Nonlinear Diffusion Filtering
11.06. Global Filters I: Discrete Variational Methods
14.06. Global Filters II: Continuous Variational Methods
18.06. Global Filters III: Deconvolution Methods
21.06. Texture Analysis

 

PART III: COMPUTER VISION AND IMAGE UNDERSTANDING

Date Topic
25.06. Image Sequence Analysis
28.06. 3-D Reconstruction I: Camera Geometry
02.06. 3-D Reconstruction II: Stereo
05.07. 3-D Reconstruction III: Shape-from-Shading
09.07. Segmentation
12.07. Object Recognition I: Hough Transform and Invariants
16.07. Object Recognition II: Eigenspace Methods
19.07. Object Recognition III: Neural Networks
23.07. Object Recognition IV: Deep Learning
26.07. Summary, Conclusions, Outlook
 

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

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.

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