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Computer Vision Group
Faculty of Informatics
Technical University of Munich

Technical University of Munich

Home Teaching Summer Semester 2011 Stasticial Methods and Learning in Computer Vision

Stasticial Methods and Learning in Computer Vision

SS 2011, TU München

Location: 02.09.023
Time and Date: every Thursday, 3.15 pm
Lecturer: Dr. Claudia Nieuwenhuis
Start: 5th of May

Statistics is the foundation of many powerful tools in computer vision. This lecture will cover a number of widely used and important techniques for the analysis of images. We will discuss a selected number of approaches concerning their mathematical theory and implementation details. Topics will cover

  • necessary basics in measure theory and statistics, e.g. measures, distributions, densities, conditional distributions, marginal distributions, cumulative distribution functions, statistical tests, p-values
  • density estimation (parametric and non-parametric) and sampling methods such as Parzen density estimation, mixture of Gaussians, EM-algorithm, particle filtering, e.g. with application to image segmentation and tracking
  • subspace methods such as principal component analysis, idependent component analysis, linear discriminant analysis, e.g. with application to face recognition
  • learning and classification approaches such as Support Vector Machines, Neural Networks, Graphical Models and Dictionary Learning

The lectures will be held in English.


Location: 02.09.023
Time and Date: every other Tuesday, 2.15 pm
Organization: Eno Töppe
Start: 17th of May

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Computer Vision Group

Boltzmannstrasse 3
85748 Garching