This CCV course was composed of lectures, two assignments, tutorials (introducing into OpenCV and the assignments, and also discussing exercises in the book), three seminars (students presenting their solutions to assignments), a review of all the taught material prior to the exam date, and a written exam.

The lectures followed a draft of the book Concise Computer Vision by Reinhard Klette, published in January 2014 by Springer, London, UK. The lecture notes below were created based on a draft of the CCV book (prior to finalising the structure, and also prior to the editorial book editing by the publisher).

1. Basics
2. Fourier

   Assignment 1

3. Normalization etc.
4. Advanced Edge Detection
5. From Adjacency to Regions
6. Image Value Analysis
7. 3D Motion and Optic Flow
8. Horn-Schunck etc.
9. Segmentation 1
10. Segmentation 2
11. Segmentation 3
12. Color
13. Cameras
14. Camera Calibration

    Assignment 2

15. Surfaces and Structured Light
16. Stereo Vision and PMS
17. PMS and Vector Field Integration
18. Stereo Matching 1
19. Stereo Matching 2
20. Stereo Matching 3
21. Tracking 1
22. Tracking 2
23. Tracking 3
24. Particle Filter
25. Kalman Filter
26. Object Detection
27. Haar-features, Viola-Jones, AdaBoost
28. Random Decision Forests, Pedestrian Detection
29. Review (of the lectures, tutorials, and assignment) by discussing

    Exercises, Part I and Exercises, Part II as given in the book, and also

    simpler questions in Exam Preparation, Part I and Exam Preparation, Part II for preparation for the written exam; the written exam about this CCV course was a (slightly modified) subset of those simpler questions on those two lists.

FURTHER MATERIAL
Intro into OpenCV by Gady Agam

FREE SYSTEMS or SOURCES
GIMP and ImageJ
Software-link on The Computer Vision Homepage.