Welcome to the homepage of the lecture

Image Processing and Computer Vision

Winter term 2012 / 2013

Image Processing and Computer Vision

Prof. Joachim Weickert
Office hour: Tuesday, 14:15 - 15:15.

Coordinator of tutorials: Pascal Peter

Winter Term 2012 / 2013

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

Lectures: Tuesday and Thursday, 10-12 c.t., Building E1.3, Lecture Hall 002

First lecture: Tuesday, October 16, 2012

Tutorials: 2 hours each week; see below.

NEWS: The results of the second written exam are now online.

NEWS: Opportunity for exam inspection:
Tuesday, April 16, 3:15 pm - 5:00 pm, Room 4.10, Bld. E1 7

Type of LecturesPrerequisitesTutorialsRegistrationWritten ExamContents Material for the Programming Assignments Literature

Broad introduction to mathematically well-founded 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 and artificial intelligence. The classes qualify for starting a bachelor's thesis in our group.

The lectures are continued in the summer term by the course "Differential Equations in Image Processing and Computer Vision" which leads to current research topics. Both courses are necessary in order to pursue a master's thesis in our group.

This course is suitable for students of visual computing, mathematics, computer science, bioinformatics, computer and communications technology, and physics. It counts e.g. as a visual computing core course within the visual computing programme, an applied mathematics course within mathematics, or a core course (Stammvorlesung) in computer science.

It is based on mathematical knowledge from the first two semesters. For the programming assignments, some elementary knowledge of C is required. The lectures are given in English.

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 permitted, but all persons must be in the same tutorial group.

If you have questions concerning the tutorials, please do not hesitate to contact Pascal Peter. The tutorials are conducted by Leif Bergerhoff, Mohamed Omran, Sarah Schaeffer and Martin Schmidt.

Seven groups are scheduled for Tuesday and Wednesday afternoon:

  • Group T1:
    Tue, 14-16, Building E1.3, Seminar Room 014

  • Group T2:
    Tue, 14-16, Building E1.3, Seminar Room 016
    (also for honour's programme)

  • Group T3:
    Tue, 16-18, Building E1.3, Seminar Room 014

  • Group W1:
    Wed, 14-16, Building E1.3, Seminar Room 014
    (in German)

  • Group W2:
    Wed, 14-16, Building E1.3, Seminar Room 107

  • Group W3:
    Wed, 16-18, Building E1.3, Seminar Room 016

  • Group W4:
    Wed, 16-18, Building E1.3, Seminar Room 107

If you have difficulties with the programming assignments, feel free to participate in

  • Optional Guided Programming (OGP):
    Tue, 18-20, Building E1.3, CIP Room 012
    Tutor: Pascal Peter

The tutors can be reached via the mail addresses:
ipcv-# -- at -- mia.uni-saarland.de
where # has to be replaced by t1, t2, t3, w1, w2, w3, w4, and ogp, respectively.

Registration is closed. It was open from Tue, Oct. 16, 2012, 3 pm until Fri, Oct. 19, 2012, 3 pm. You can now check which group you are finally in.

The results of the first written exam can be found here, and the corresponding distribution of points and grades here.

The results of the second written exam can be found here, and the corresponding distribution of points and grades here.

You can pick up certificates from Gabriele Voss in Building E2.4, Room 111.

Course material is available on this webpage in order to support the classroom 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.


Date Topic
16.10. Foundations I: Definitions, Image Types, Discretisation
18.10. Foundations II: Degradations in Digital Images
(contains classroom assignment C1 and homework H1)
23.10. Foundations III: Colour Perception and Colour Spaces
25.10. Image Transformations I: Continuous Fourier Transform
(corrected version)

(contains classroom assignment C2 and homework H2)
30.10. Image Transformations II: Sampling Theorem and Discrete Fourier Transform
05.11. Image Transformations III: Discrete Cosine Transform and Image Pyramids (corrected version)
(contains classroom assignment C3 and homework H3)
06.11. Image Transformations IV: Discrete Wavelet Transform
08.11. Image Compression
(contains classroom assignment C4 and homework H4)
13.11. Image Interpolation


Date Topic
15.11. Point Operations
(contains classroom assignment C5 and homework H5)
20.11. Linear Filters I: System Theory
22.11. Linear Filters II: Derivative Filters
(contains classroom assignment C6 and homework H6)
27.11. Linear Filters III: Detection of Edges and Corners
29.11. Nonlinear Filters I: Morphology and Median Filters
(contains classroom assignment C7 and homework H7)
04.12. Nonlinear Filters II: Wavelet Shrinkage, Bilateral Filters, NL-Means
06.12. Nonlinear Filters III: Nonlinear Diffusion Filtering
(contains classroom assignment C8 and homework H8)
11.12. Global Filters I: Discrete Variational Methods
13.12. Global Filters II: Continuous Variational Methods
(contains classroom assignment C9 and homework H9)
18.12. Global Filters III: Deconvolution Methods
20.12. Texture Analysis
(contains classroom assignment C10 and homework H10)


Date Topic
08.01. Segmentation I: Thresholding, Region Growing, Region Merging
10.01. Segmentation II: Watersheds and Optimisation Methods
(contains classroom assignment C11 and homework H11)
15.01. Image Sequence Analysis I: Local Methods
17.01. Image Sequence Analysis II: Variational Methods
(contains classroom assignment C12 and homework H12)
22.01. 3-D Reconstruction I: Camera Geometry
24.01. 3-D Reconstruction II: Stereo
(contains classroom assignment C13 and homework H13)
29.01. 3-D Reconstruction III: Shape-from-Shading
31.01. Object Recognition I: Hough Transform and Invariants
05.02. Object Recognition II: Eigenspace Methods
07.02. Summary, Conclusions, Outlook

The following self-test problem sheet contains 6 problems, which are intended to be similar in style and difficulty to a 180-minutes written exam.

31. 01. Self Test Problem Sheet
31. 01. Self Test Solution

Here you can download material for the programming assignments.

18. 10. Assignment H1: Noise, Quantisation and Dithering
25. 10. Assignment H2: Colour Spaces and Subsampling
01. 11. Assignment H3: Fourier Analysis and Discrete Cosine Transform
08. 11. Assignment H4: Bitplane Coding
15. 11. Assignment H5: Point Transformations
22. 11. Assignment H6: Linear Filters
29. 11. Assignment H7: Edge and Corner Detection, Morphology
06. 12. Assignment H8: Wavelet Shrinkage, NL-Means
13. 12. Assignment H9: Whittaker-Tikhonov Regularisation, Unsharp Masking
22. 12. Assignment H10: Deconvolution
10. 01. Assignment H11: Toboggan Watershed Segmentation
17. 01. Assignment H12: Optic Flow
24. 01. Assignment H13: Correlation-Based Stereo Method

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, 2006.
  • R. C. Gonzalez, R. E. Woods: Digital Image Processing. Addison-Wesley, Third Edition, 2008.
  • K. D. Tönnies: Grundlagen der Bildverarbeitung. Pearson Studium, München, 2005.

Computer vision books include

  • E. Trucco, A. Verri: Introductory Techniques for 3-D Computer Vision. Prentice Hill, Upper Saddle River, 1998.
  • R. Klette, K. Schlüns, A. Koschan: Computer Vision: Three-Dimensional Data from Images. Springer, Singapore, 1998.
  • R. Szeliski: Computer Vision: Algorithms and Applications. Springer, New York, 2010.

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.
General information and numerous links can be found at the Computer Vision Homepage. If you are looking for a specific reference, check out Keith Price's Annotated Computer Vision Bibliography. Many online articles and citations can be extracted from the CiteSeer webpage.

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