Computer Vision - Image Understanding - Bildverstehen
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.
Office hours: Mondays between 13:00 and 14:00
Exam WS 2016-2017
The grades should be on SBService soon. Access to the examination papers is only during the office hours: Monday 13:00 - 14:00.
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
- Machine learning
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.
Slides for the lecturesChapter 01 - Introduction (pdf)
Chapter 02 - Image Formation (pdf)
Chapter 03 - Image processing (pdf)
Chapter 04 - Geometric transformations (pdf)
Chapter 05 - Feature detection and matching (pdf)
Chapter 06 - Segmentation (pdf)
ExercisesExercise 01 - Introduction to Python and NumPy. Text - Data - Solution.
Exercise 02 - Basic image processing. Text - Data - Solution.
Exercise 03 - Linear Filtering. Text - Solution.
Exercise 04 - Edge detection. Text - Data - Solution.
Exercise 05 - Fourier transforms. Text - Data - Solution.
Exercise 06 - Face Swapping. Text - Data - Solution.
Exercise 07 - Feature detection and matching. Text - Data - Solution.