Model Learning
Topic Who
(email, Name)
Location Title Lectures Students Level Resources
(printed, online, other)
1.1.
1.2.

Hopfield, John
Hopfield Group
Princeton University
Princeton, USA
Neural Network . . .
Syllabus
1.1
1.2
liedtke@tnt.uni-hannover.de
Liedtke, Claus-E.
University of Hannover
Hannover, Germany
Pattern Recognition 30 + 15 exercises 20-30 intermediate
all about the Lecture
1. pinz@emt.tu-graz.ac.at
Pinz, Axel
Graz University of Technology
Graz, Austria
Image Understanding 2 semester-hours lecture, 1 semester-hour practice 20 intermediate book "Bildverstehen", Springer Verlag, 1994

1.1.4. petia@cvc.uab.es
Radeva, Petia
Computer Vision Center, University of Barcelona
Barcelona, Spain
Model-Based Analysis 10 15 intermediate advanced powerpoint presentation

1.1.2./4. scheun@ruca.ua.ac.be
Scheunders, Paul
Vision Lab, University of Antwerp
Antwerp, Belgium
Artificial Neural Networks 20 5-10 advanced

1.1.
1.2.
milan-sonka@uiowa.edu
Sonka, Milan
University of Iowa
Iowa, USA
Digital Image Processing . . intermediate Syllabus Lecture
1.1..
1.2
Jordi.Vitria@uab.es
Vitrià, Jordi
Computer Vision Center, University of Barcelona
Barcelona, Spain
Computational Vision 15 75 intermediate - Emanuele Trucco, Alessandro Verri. Introductory Techniques for 3-D Computer Vision. (March 6, 1998) Prentice Hall; ISBN: 0132611082 - J.Vitria, Visió per Computador , Servei Publicacions de la UAB, 1995, ISBN 84-490-0295
Lecture description (in Catalan)
1.1.5. swachsmu@TechFak.Uni-Bielefeld.DE
Wachsmuth, Sven
Faculty of Technology of Bielefeld University
Bielefeld, Germany
Pattern Analysis 14 40-50
40-50
intermediate German lecture scripts


Last modified: Thu Sep 12 11:16:00 GMT 2002