Literatur |
Lehrbücher:
- Milan Sonka, Vaclav Hlavac, Roger Boyle (2008): Image Processing, Analysis, and Machine Vision, Thomson.
-John C. Russ, J. Christian Russ (2008): Introduction to Image Processing and Analysis, CRC Press.
-R. C. Gonzalez, R. E Woods (2008): Digital Image Processing, Pearson.
- David R. Forsyth, Jean Ponce (2003): Computer Vision: a Modern Approach, Prentice Hall.
- G. Aubert, P. Kornprobst (2006): Mathematical Problems in Image Processing. Partial Differential Equations and the Calculus of Variations, Springer.
-J. R. Parker (1997): Algorithms for Image Processing and Computer Vision, Wiley. |
Lerninhalte |
The course will cover statistical data-driven approaches for automatic processing, analyzing and understanding of images. The lecture will cover topics from the pre-processing of images, like image filtering and feature detection to object recognition and object tracking as well as image classification.
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