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Shape Modelling: A Machine Learning Approach
Course Number  3952501 
Lecturers  Marcel Lüthi, Thomas Vetter 
Time and Location  Wed 08:15  10:00; Seminarraum 00.003, Spiegelgasse 1 Wed 10:00  11:00; ComputerLabor U1.001, Spiegelgasse 1 Thu 09:00  10:00; ComputerLabor U1.001, Spiegelgasse 1 
Start  18022015 
Prerequisites  No specific formal requirements. Though students should have basic knowledge and skills in the fields of linear algebra, probability theory, statistics, and programming.

Objectives  Upon successful completion of the course the students are able to:
 apply the theory of Gaussian processes for shape modeling  understand how methods from machine learning can be used to learn properties of shapes  implement a basic system for image analysis and shape modeling  describe different methods for shape modeling  describe different methods for image registration and see the connections between different classes of algorithms 
Contents  The course focus is on the problem of modelbased image analysis. We discuss some basic approaches for image analysis and in particular focus on Analysis by synthesis. This approach is based on the assumption that in order to be able to analyse an image, one also needs to be able to synthesize this image by means of probabilistic models. Key questions of this course are how we can build such models, and use them for analysing (medical) images and the reconstruction of partial data.
We draw on the theory of Gaussian processes and study their application for modelling shapes. We take on a machine learning perspective, where the topic of modeling with Gaussian processes has been explored in depth and many algorithms and theoretical results have been established. By using those methods for shape modeling, the concepts can be visualized and thus it will be easier to obtain a good intuition of these methods. This aspect will in particular be covered in the exercises, where the theoretical methods are implemented to build a complete framework for modelbased image analysis. The exercises will be done in the programming language Scala, but no prior knowledge of Scala is required. The theory of Gaussian processes and Reproducing Kernel Hilbert spaces can be used to unify many distinct methods currently used in image analysis. We aim at making wherever possible the connections to popular research approaches in image analysis, shape modelling and registration, such that a student gets a good overview and understanding of the methods used in this field. 
Literature  Gaussian processes for machine learning, Carl Edward Rasmussen, 2006 (online version)
Pattern theory: from representation to inference, Ulf Grenander and Michel I Miller, 2007 Statistical Shape Analysis: Ian L. Dryden, Kanti V. Mardia, 1998 
Assessment  Lehrveranst.begleitend Please note: Student assessment will be based on a group project and a written report. Small student groups will implement a system for modelbased segmentation of an anatomical structure. Organized as a competition between groups, the performance of each system will be evaluated using a publicly available benchmark dataset. A report, which summarizes the research findings in the project, will need to be handed in individually. The grading will be based on both, the group performance on the project and the scientific quality of the individual report. 
Credit Points  4 
Grades  16 0,5 
Modules  Vertiefungsmodul Bioinformatik (Bachelor Informatik 07) Vertiefungsmodul Geoinformatik (Bachelor Informatik 07) Modul Wahlbereich Informatik (BSF  Informatik) Modul Praxis aktueller Informatikmethoden (MSF  Informatik) Vertiefungsmodul Computational Intelligence (Bachelor Informatik 10) Vertiefungsmodul Life ScienceInformatik (Bachelor Informatik 10) 
Registration  Services (Requires login) 
Course webpage
See the course webpage for links to the slides, exercises and software tutorial.