Contact
+4991318527775
+4991318527270
Secretary
Monday  8:00  12:15 
Tuesday  8:00  16:45 
Wednesday  8:00  16:45 
Thursday  8:00  16:45 
Friday  8:00  12:15 
Address
FriedrichAlexanderUniversität ErlangenNürnberg (FAU)
Lehrstuhl für Informatik 5 (Mustererkennung)
Martensstr. 3
91058 Erlangen
Germany
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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, distributionfree 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:Tuesday, 10:15  11:45; Room: A 2.16 Wednesday, 12:15  13:45; Room: 0.68
 Tentative course syllabus: It provides a roadmap to the lectures throughout the semester.
 The lecture video is available here (only accessible if you are inside the university network; if you want to watch the videos from home, consider to tunnel the connection).
A. Exam Dates Monday 18.02.2013 (only in the morning) Tuesday 19.02.2013 Wednesday 20.02.2013 (only in the morning) Wednesday 20.03.2013 Thursday 21.03.2013 Tuesday 09.04.2013 Wednesday 10.04.2013 B. Signing up for the Exam Reserving a slot for the exam is only possible after January 6th, 2013. You must reserve a timeslot for the exam, independent of whether you have signed up at meinCampus. You can do so by: 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, number of credits and type of exam (e.g. benoteter Schein, unbenoteter Schein, Prüfung durch meinCampus, etc.).
The updated slides will be posted on the web soon after the corresponding lecture is completed.
In order to prepare yourself for an upcoming lecture, look at the slides of the previous Winter semester. Introduction:  course outline  Key PR Concepts:  the pipeline of a PR system, terminology, postulates of PR  Sampling:  review of Fourier analysis, Nyquist sampling theorem  Quantization:  signaltonoise ratio, pulse code modulation, vector quantization, kmeans algorithm  Equalization and Thresholding:  histogram equalization, thresholding, binarization, maximum likelihood estimation, various thresholding algorithms (intersection of Gaussians, Otsu's algorithm, unimodal algorithm, entropybased)  Noise Suppression:  Linear Shift Invariant transformations, convolution, mean filter, Gaussian filter, median filter  Edge Detection:  gradientbased edge detector, Laplacian of Gaussian, sharpening  Nonlinear Filtering:  recursive filters, homomorphic filters, cepstrum, morphological operators, rank operators  Pattern Normalization:  size normalization, location normalization, pose normalization, geometric moments, central moments  Introduction to Feature Extraction:  curse of dimensionality, heuristic versus analytic feature extraction methods, projection on orthogonal bases, Fourier transform as a feature  Orthonormal Bases for Feature Extraction:  spectrogram, WalshHadamard transform, Haar transform  LPC and Moments:  linear predictive coding, moments as features, Hu moments  Multiresolution Analysis:  shorttime fourier transform, continuous wavelet transform, discrete wavelt transform, wavelet series  PCA, LDA:  introduction to analytic feature extraction, principal component analysis, eigenfaces, linear discriminant analysis, fisherfaces  OFT:  optimal feature transform, Mahalanobis distance, feature transform  Optimization Methods:  gradient descent, coordinate descent  Feature Selection:  objective functions for feature selection including entropy and KLdivergence, strategies for exploring the space of feature subsets including branchandbound  Bayesian Classifier:  introduction to classification, decision function, misclassification cost, misclassification risk, Bayesian classifier  Gaussian Classifier:  Gaussian classifier, linear vs. quadratic decision boundaries  Polynomial Classifiers:  polynomial classifier, discriminant functions  Nonparametric Classifiers:  Knearest neighbor density estimation, Parzen windows  Artificial Neural Networks:  introduction to ANNs, ANN and classification, Radial Basis Function ANNs  Multilayer Perceptrons:  ANN layouts, feedforward networks, perceptron, MLPs, backpropagation  Review:  end of lecture review, brief recap of what was covered in class 
 A nice review/tutorial on Fourier Analysis can be found at http://www.sunlightd.com/fourier/
 Follow this link for additional information on Covariance Matrices.
 A more detailed tutorial on Principal Component Analysis can be found here.
 Here are some notes on Singular Value Decomposition.
 Here is the pseudocode for feature selection using Branch and Bound.
 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 website. P.M. Narendra and K. Fukunaga, "A Branch and Bound Algorithm for Feature Subset Selection, " IEEE Transactions on Computers, Vol. C26, No. 9, 1977, pp. 917922.
 A general overview on the Branch and Bound methodology is presented here.
 A detailed paper on Branch and Bound can be found in this webpage.
