Dept. of Computer Sc. » Pattern Recognition » Courses » WS 11/12 » Introduction to Pattern Recognition [IntroPR]
Introduction to Pattern Recognition [IntroPR]
The goal of this lecture is to familiarize the students with the overall pipeline of a Pattern Recognition System. The various steps involved from data capture to pattern classification are presented. The lectures start with a short introduction, where the nomenclature is defined. Analog to digital conversion is briefly discussed with a focus on how it impacts further signal analysis. Commonly used preprocessing methods are then described. A key component of Pattern Recognition is feature extraction. Thus, several techniques for feature computation will be presented including Walsh Transform, Haar Transform, Linear Predictive Coding, Wavelets, Moments, Principal Component Analysis and Linear Discriminant Analysis. The lectures conclude with a basic introduction to classification. The principles of statistical, distribution-free and nonparametric classification approaches will be presented. Within this context we will cover Bayesian and Gaussian classifiers, as well as artifical neural networks. The accompanying exercises will provide further details on the methods and procedures presented in this lecture with particular emphasis on their application.
Dates & Rooms:
Tuesday, 12:15 - 13:45; Room: 0.68
Wednesday, 12:15 - 13:45; Room: 0.68
A tentative syllabus can be found here.
A. Exam Dates
Special Date for people not using Mein Campus: Thursday 08.03.2012
B. Signing up for the Exam
You must reserve a time slot for the exam. You can do so :
either by personally visiting the secretaries at the Pattern Recognition Lab, at the 09.138 at Martenstr. 3, 91058 Erlangen.
or by sending them an email at Kristina Müller at mueller(at)cs.fau.de or at Iris Koppe at koppe(at)cs.fau.de . Make sure in your email to write your full name, student ID, program of Studies, birthdate, type of exam and number of credits (e.g. benoteter Schein, unbenoteter Schein, Prüfung durch meinCampus, etc.).
Students who are interested in hands-on PR experience and an additional 2.5 ECTS credits, can work on one of the following projects:
For more details, contact me directly.
On Wednesday 19.02.2012 there will be a tour of the different on-going projects at the Patter Recognition lab. This is a great opportunity to familiarize yourself with the work performed at LME. It is a must if you are considering doing your Bachelor or Master's thesis at LME.
1. Follow this link for additional information on Covariance Matrices.
2. A more detailed tutorial on Principal Component Analysis can be found here.
3. Here is the pseudocode for feature selection using Branch and Bound.
4. You may want to read the following paper which describes how Branch and Bound can be used in Feature Selection. Due to copyright issues, a copy can not be placed on this web-site.
P.M. Narendra and K. Fukunaga, "A Branch and Bound Algorithm for Feature Subset Selection, " IEEE Transactions on Computers, Vol. C-26, No. 9, 1977, pp. 917-922.
5. A general overview on the Branch and Bound methodology is presented here.
6. A detailed paper on Branch and Bound can be found in this web-page.