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 artificial 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:Wednesday, 13:00 - 13:45; Room:
0.68 Tuesday, 12:15 - 13:45; Room:
0.68
