Techn. Fakultät Willkommen am Institut für Informatik FAU-Logo


Date and Time

The exercises take place on

Monday, 16:15-17:00

Wednesday, 16:00-16:45

in room 09.150 (computer science building, seminar room of the Pattern Recognition Lab).

Exercise sheets

Please prepare the exercises at home. We will put the exercises online a couple of days (typically more than 5) prior to the class.

Exercise 1Statistics
Exercise 2Quantization
Exercise 3ML-Estimation and Binarization (you will also need the test image "object"!)
Exercise 4Image enhancement (and test image "moon")
Exercise 5Fourier series, histogram equalization (featuring Lena in color and black and white \o/)
Exercise 6Everything about moments! (with the test images momented and unmomented)
Exercise 7Linear predictive coding
Exercise 8Wavelet decomposition - Note that this is a coding-only exercise! Q: Programming only? Should I ignore it? A: Omg, NO!  --> better go ahead and download the supplemental material :)
Exercise 9Principal component analysis and testing data
Exercise 10Branch&bound
(note that the fixed point theorem has been removed)
Exercise 11Classification (-> re-review this work sheet, Exercise 28 has been clarified & expanded!)

Supplemental Material

Some facts on the Fourier transform.

Some facts on statistics.


A brief explanation on the variance of the estimated mean (Exercise 9). A participant had a question on an identity that can be derived using the Bienayme formula. So relax - everything is sane :)


...and a comment on the repetition of the first row at the end of the matrix in the discrete Haar transform (worksheet 8, wavelet transform). The result of conv2 should contain as much entries as the input (in order not to lose information during the transformation). Thus, as we have a filter of width 2, we would lose one line in the output, if the wrap-around of the matrix entries weren't added. However, in our example application, this loss "would not hurt much" (-> try it), but it is still imprecise.