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Professur Künstliche Intelligenz

Computer Vision - Image Understanding - Bildverstehen

General Information

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

Content

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).

  1. Introduction
  2. Image formation
  3. Image processing
  4. Geometric transformations
  5. Feature detection and matching
  6. Segmentation
  7. Motion, optical flow
  8. Machine learning

The exercises are made on computers using the OpenCV computer vision library with the Python bindings.

References

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.

For German-speaking students, the book "Bildanalyse" by my predecessor Dr. Johannes Steinmüller will be very helpful.



Slides for the lectures

Chapter 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)

Exercises

Exercise 01 - Introduction to Python and NumPy. (pdf , data , solution )
Exercise 02 - Basic image processing. (pdf , data , solution )
Exercise 03 - Linear Filtering. (pdf , solution )
Exercise 04 - Edge detection. (pdf , data , solution )
Exercise 05 - Fourier transforms. (pdf , data , solution )
Exercise 06 - Face Swapping. (pdf , data , solution )
Exercise 07 - Feature detection and matching. (pdf , data , solution )

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