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Slides

This material is copyrighted (c) 2014-2016 by Dr.-Ing. Stefan Steidl, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany. It is based on the former lectures Mustererkennung I & II by Prof. Dr.-Ing. Heinrich Niemann and the replacing lectures by Prof. Dr.-Ing. Joachim Hornegger and Elli Angelopoulou, PhD. Use without prior written permission of the authors is not permitted!

 

 

These slides are part of a presentation. The presentation is not complete without the verbal explanations given during the lecture.

The pdf files are password protected. You will get the password during the first lecture.

Three different versions of the slides are available:

  1. The 'screen' version are the slides presented during the lectures. They may contain embedded video and audio files leading to large file sizes. Furthermore, they use extensively overlays, i.e. slides are displayed incrementally, resulting in a sequence of pdf pages instead of a single one just showing the final outcome. Hence, this version is not suited for printing. The slides are best viewed with a software that is able to show overlays without transition effects (e.g. GoodReader for iPad).
  2. 'Print (A5)' shows two slides per page and is intended for printing booklets in the DIN A5 format. Let the printer driver rearrange the pages and use duplex printing. The printed versions do not contain video and audio files and most important, they do not have overlays. The printed versions include some slides that are especially designed for printing: for example, in a presentation, the difference between two similar images can be best viewed if both images are overlaid. As this is not possible in a print-out, both images are printed next to each other instead.
  3. 'Print (A6)' shows four slides per page. Besides the different arrangement of slides per page, the slides are identical to the other print version. This is the standard option for printing if you want to file your print-outs in a folder.

We will update the slides during the semester as needed in order to improve the quality of our slides.
Please make sure that you have the latest version of our slides in preparation for the exam!

 

 

Chapter Lecture

Slides

Date (Week) SlidesScreen
Print (A5)Print (A6) Update
1 Introduction 17.10.2017 (1) 1-1 - 1-55 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf10.10.2017
2 Key concepts of pattern recognition18.10.2017 (1)2-1 - 2-25 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf10.10.2017
24.10.2017 (2)2-26 - 2-37
3 Analog to digital conversion: sampling24.10.2017 (2)3-1 - 3-17 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf19.10.2017
25.10.2017 (2)3-19 - 3-28
31.10.2017 (3)

01.11.2017 (3)
07.11.2017 (4)3-29 - 3-46
08.11.2017 (4)3-47 - 3-50
4 Analog to digital conversion: quantization08.11.2017 (4)4-1 - 4-19 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf07.11.2017
14.11.2017 (5)4-20 -4-30
5 Histogram equalization14.11.2017 (5)5-1 - 5-18 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf07.11.2017
6 Thresholding and binarization15.11.2017 (5)6-1 - 6-25 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf13.11.2017
21.11.2017 (6)6-26 -6-43
7 Filtering: noise suppression21.11.2017 (6)7-1 - 7-15 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf15.11.2017
22.11.2017 (6)7-16 - 7-41
28.11.2017 (7)7-42 - 7-48
8 Edge detection28.11.2017 (7)8-1 - 8-31 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf21.11.2017
29.11.2017 (7)8-32 - 8-44
9 Non-linear filtering29.11.2017 (7)9-1 - 9-9 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf05.12.2017
05.12.2017 (8)9-10 - 9-56
10Normalization06.12.2017 (8)10-1 - 10-23 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf29.11.2017
12.12.2017 (9)10-24 - 10-27
11Feature extraction: heuristic methods12.12.2017 (9)11-1 - 11-24 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf29.11.2017
13.12.2017 (9)11-25 - 11-53
19.12.2017 (10)11-54 - 11-95
20.12.2017 (10)11-96 - 11-131
09.01.2018 (11)11-132 - 11-183
10.01.2018 (11)11-184 - 11-198
12Feature extraction: analytic methods16.01.2018 (12)12-1 - 12-16 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf09.01.2018
17.01.2018 (12)12-17 - 12-28
23.01.2018 (13)12-29 - 12-60
24.01.2018 (13)12-60 - 12-65
13Numerical optimization24.01.2018 (13)13-1 - 13-10 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf19.01.2018
30.01.2018 (14)13-11 - 13-22
14Feature selection Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf19.01.2018
15Statistical classification30.01.2018 (14)15-1 - 15-27 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf19.01.2018
31.01.2018 (14)15-28 - 15-42
16Polynomial classifier06.02.2018 (15)16-1 - 16-24 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf19.01.2018
17Non-parametric classifiers06.02.2018 (15)17-1 - 17-31 Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf19.01.2018
18Kalman filter Initiates file downloadpdfInitiates file downloadpdfInitiates file downloadpdf19.01.2018

 

Additional material

Python code examples are shown during the lecture.
The algorithms are demonstrated using IPython Notebook.

Getting familiar with the algorithms is essential and will be part of the oral exams. This is true for both the 5.0 ECTS and the 7.5 ECTS exams. Please make sure that you have the latest version of the files as they might be updated.