Probabilistic Fitting

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Fitting parametric models to novel data is a common problem in many disciplines. In Computer Vision and Medical Image Analysis such optimization tasks are often difficult due to the large number of model parameter and additionally the problem of local minima of the fitting problem.

In this free online course, you will get insights how probabilistic methods can overcome these difficulties. You will learn how the optimization problem can be reformulated in a fully probabilistic form using "Bayes-Theory". Based on this formalism you will understand that a data-driven Markov Chain Monte Carlo optimization technique is well suited for the problem.

Course Material:

During the tutorial you will implement a simple framework to reconstruct a face from a single photograph.

The structure of this tutorial is similar to the Statistical Shape Modelling course, it consists of:

All course material is wrapped into single tutorial file that must be downloaded as a whole:

Download Probabilistic Fitting Tutorial

Requirements

Memory Issues

To prevent memory issues, you can launch the tutorial using the extra JVM flag-Xmxwith an explicitly set amount of memory. Use 2g for optimal results.

Start the tutorial with the command:

java -Xmx2g -jar scalismoLab-faces.jar