Welcome to the homepage of the lecture

Image Processing and Computer Vision

Winter term 2007 / 2008

Image Processing and Computer Vision

Prof. Joachim Weickert
(Building E1.1, Room 3.11, Phone 0681-302-57340)

Winter term 2007 / 2008

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

Lectures: Tuesday and Thursday, 10-12 c.t., Building E13, Lecture Hall 002

First lecture: Tuesday, October 23, 2007

Tutorials: 2 hours each week; see below.

TutorialsPrerequisitesTypes of LecturesTutorialsWritten ExamContentsMaterial for the Programming AssignmentsLiterature

6 groups are scheduled for Tuesday and Wednesday:

  • Group T1: Tue, 14-16
    Bldg. E1 3, seminar room 16 (theory); Bldg. E1 3, CIP 105 (programming)
  • Group T2: Tue, 14-16
    Bldg. E1 3, seminar room 14 (theory); Bldg. E2 4, CIP U009 (programming)
  • Group T3: Tue, 16-18,  also honours programme (Förderstudierende)
    Bldg. E1 3, seminar room 16 (theory); Bldg. E1 3, CIP 105 (programming)
  • Group T4: Tue, 16-18,  in German
    Bldg. E1 3, seminar room 14 (theory); Bldg. E2 4, CIP U009 (programming)
  • Group W1: Wed, 14-16
    Bldg. E1 3, seminar room 16 (theory); Bldg. E2 4, CIP U009 (programming)
  • Group W2: Wed, 16-18
    Bldg. E1 3, seminar room 14 (theory); Bldg. E2 4, CIP U009 (programming)

Registration: Registration is closed. It was open from Tue, Oct. 23, 2007, 15:00h until Fri, Oct. 26, 2007, 15:00h.

This course is suitable for students of visual computing, mathematics or computer science. It counts either as a theoretical core course (Theorie-Stammvorlesung) in computer science or as an applied mathematics course. 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.

Broad introduction into mathematically well-founded areas of image processing and computer vision. These fields are important in numerous applications including medical imaging, computer-aided quality control, robotics, computer graphics, multimedia and artificial intelligence. The classes qualify for starting a bachelor thesis in our group.
The lectures are continued in the summer term by the in-depth course "Differential Equations in Image Processing and Computer Vision" which leads to current research topics. Both classes are necessary in order to pursue a diploma or master thesis in our group.

The tutorials include programming and theoretical 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. If you have questions concerning the tutorials, please do not hesitate to contact Dr. Andrés Bruhn. The tutorials are conducted by Thomas Bühler, Oliver Demetz, Luis Pizarro, and Sebastian Zimmer.

The first written exam took place on Friday, February 29, 2008 from 2:00 to 5:00 pm.
The second written exam took place on Wednesday, April 9, 2008 from 2:00 to 5:00 pm.
In order to qualify for the exams you need 50% of all points from the assignments. Theoretical and practical assignments count for the final score. In case of qualification, you are allowed to take part in both exams. The better grade counts.

These are the rules during the exams:

  • For the exams, you can use the IPCV course material (including lecture notes and example solutions from this web page) and hand-written tutorial notes, but no books.
  • Pocket calculators are not allowed.
  • Mobile phones, PDAs, laptops and other electronic devices have to be turned off.
  • Please keep the student ID card ready for an attendance check during the exam.

NEWS: the cerfificates (Scheine) for IPCV are issued by the office of the Mathematics Department. They can be obtained from Mrs. Voss, Building E2.4, Room 111 (math building, ground floor, 8.15-11.30 AM).

Course material will be made available on this webpage in order to support the classroom teaching and the tutorials, not to replace them. Additional organisational informations, 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
23.10. Definitions, Image Types, Discretisation
25.10. Degradations in Digital Images
30.10. Image Transformations I: Continuous Fourier Transform
(contains classroom assignment C1 and theoretical homework T1)
06.11. Image Transformations II: Discrete Fourier Transform
(contains programming assignment P1)
08.11. Image Transformations III: Image Pyramids
13.11. Image Transformations IV: The Discrete Wavelet Transform
(contains classroom assignment C2 and theoretical homework T2)
15.11. Color Perception and Color Spaces
20.11. Image Compression
(contains programming assignment P2)
22.11. Image Interpolation
27.11. Image Enhancement I: Point Operations
(contains classroom assignment C3 and theoretical homework T3)
29.11. Image Enhancement II: Linear Filters
04.12. Image Enhancement III: Wavelet Shrinkage, Median Filters, M-Smoothers
(contains programming assignment P3)
06.12. Image Enhancement IV: Morphological Filters
11.12. Image Enhancement V: Nonlinear Diffusion Filtering
(contains programming assignment P4)
13.12. Image Enhancement VI: Discrete Variational Methods
18.12. Image Enhancement VII: Continuous Variational Methods
(contains classroom assignment C4 and theoretical homework T4)
20.12. Image Enhancement VIII: Fourier Methods and Deconvolution
08.01. Feature Extraction I: Edges
(contains programming assignment P5)
10.01. Feature Extraction II: Edges in Multichannel Images and Corners
15.01. Feature Extraction III: Contour Representations and Hough Transform
(contains classroom assignment C5 and theoretical homework T5)
17.01. Texture Analysis
22.01. Segmentation I: Classical Methods
(contains programming assignment P6)
24.01. Segmentation II: Optimisation Methods
29.01. Image Sequence Analysis I: Local Methods
(contains classroom assignment C6 and theoretical homework T6)
31.01. Image Sequence Analysis II: Variational Methods
05.01. 3-D Reconstruction I: Camera Geometry
(contains programming assignment P7)
07.02. 3-D Reconstruction II: Stereo
12.02. 3-D Reconstruction III: Shape-from-Shading
(contains classroom assignment C7)
14.02. Object Recognition I: Invariants
19.02. Object Recognition II: Eigenspace Methods
21.02. Summary, Conclusions, Outlook

06. 11. Assignment P1: Noise Generation and Discrete Fourier Transform
20. 11. Assignment P2: Colour Spaces, Discrete Cosine Transform and JPEG
04. 12. Assignment P3: Point Transformations
11. 12. Assignment P4: Morphological Operations
08. 01. Assignment P5: Deconvolution
22. 01. Assignment P6: Corner Detection, Double Thresholding
05. 02. Assignment P7: Optic Flow

There is no specific book for this class, but most image processing topics are treated 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, Second Edition, 2002.
  • K. D. Tönnies: Grundlagen der Bildverarbeitung. Pearson Studium, München, 2005.

Specific computer vision books include

  • E. Trucco, A. Verri: Introductory Techniques for 3-D Computer Vision. Prentice Hill, Upper Saddle River, 1998.
  • R. Jain, R. Kasturi, B. G. Schunck: Machine Vision. McGraw-Hill, New York, 1995.
  • R. Klette, K. Schlüns, A. Koschan: Computer Vision: Three-Dimensional Data from Images. Springer, Singapore, 1998.

These and further books can be found in the computer science library.
Furthermore, there is an interesting online compendium, where many researchers have written survey articles.
General informations 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. Numerous citations and online articles can be extracted from the CiteSeer webpage.

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