Computer Vision - Image Understanding - Bildverstehen
|Vorlesung:||Donnerstag,||11:30 - 13:00, B006,||(Dr. J. Vitay)|
|Übung:||Montag,||19:00 - 20:30, 1/B202,||(A. Al Ali)|
|Übung:||Dienstag,||11:30 - 13:00, 1/B202,||(A. Al Ali)|
Prerequisites: Modules in Mathematics I to IV, basic knowledge in Python.
Exam: written examination (90 minutes), 5 credit points.
Contact: julien dot vitay at informatik dot tu-chemnitz dot de.
Language: English. The written exam can of course be done in German.
The course is an introduction to computer vision, from basic image processing (filters, Fourier transformation) to more advanced algorithms (object recognition, movement, facial features extraction).
- Image formation
- Image processing
- Geometric transformations
- Feature detection and matching
- Motion, optical flow
The exercises are made on computers using the OpenCV computer vision library with the Python bindings.
The course is based primarily on the textbook by Richard Szeliski:
Richard Szeliski (2010). Computer Vision: Algorithms and Applications. Springer.
A free online draft is available at szeliski.org/Book/
The online book by Simon Prince, Computer vision: models, learning and inference (available at computervisionmodels.com) might also be helpful.
- How do I register for the course?
You don't. There is no OPAL or anything, just show up to the lectures/exercises and register for the exam in December.
- How do I register for the exam?
Registration on SBService happens in December. Only registered students can participate to the exam.
- I cannot assist to the exercises. Can I take the exam anyway?
Yes. The exercises are there to help you understand the concepts seen in the lectures and get practical experience with computer vision tools. But they are not obligatory for the exam.
- Do I have to memorize all these equations?
No, but to understand them, which is basically the same.
Slides for the lecturesChapter 01 - Introduction (pdf)
Chapter 02 - Image Formation (pdf)
Chapter 03 - Image processing (pdf)
Chapter 04 - Geometric transformations (pdf)
To use Jupyter notebooks in the B202, follow these guidelines (pdf).Exercise 01 - Introduction to Python and NumPy. (1-1-pdf , 1-1-nb , 1-1-solution , 1-2-pdf , 1-2-nb , 1-2-solution )
Exercise 02 - Basic image processing. (exercise-2 , solution )
Exercise 03 - Linear Filtering. (exercise-3 , solution )
Exercise 04 - Edge detection. (exercise-4 , solution )
Exercise 05 - Fourier transforms. (exercise-5 )