|
||||||||||||||||||||||||||||||||||||||||||||||||
Website deprecated and outdated. Click here for the new site. | ||||||||||||||||||||||||||||||||||||||||||||||||
Dept. of Computer Sc. » Pattern Recognition » Courses » WS 11/12 » Introduction to Pattern Recognition [IntroPR]
Introduction to Pattern Recognition [IntroPR]Summary
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 Lecturer
General InformationA tentative syllabus can be found here. Exam Information A. Exam Dates Tuesday 14.02.2012 Thursday 29.03.2012 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.).
Project Information Students who are interested in hands-on PR experience and an additional 2.5 ECTS credits, can work on one of the following projects: 1. Vessel segmentation in MRA projection images. 2. Manual detection of the optic disk in OCT images. For more details, contact me directly.
Lab Tours 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. Slides
Supplemental Material1. 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.
|