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| Semester | Frühjahrsemester 2015 |
| Angebotsmuster | unregelmässig |
| Dozierende |
Marcel Lüthi (marcel.luethi@unibas.ch)
Thomas Vetter (thomas.vetter@unibas.ch, BeurteilerIn) |
| Inhalt | The course focus is on the problem of model-based 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 model-based 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. |
| Lernziele | 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 |
| Literatur | 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 |
| Weblink | Course website |
| Teilnahmebedingungen | No specific formal requirements. Though students should have basic knowledge and skills in the fields of linear algebra, probability theory, statistics, and programming. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | Online-Angebot obligatorisch |
| Intervall | Wochentag | Zeit | Raum |
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Keine Einzeltermine verfügbar, bitte informieren Sie sich direkt bei den Dozierenden.
| Module |
Modul Praxis aktueller Informatikmethoden (Master Studienfach: Informatik) Modul Wahlbereich Informatik (Bachelor Studienfach: Informatik) Vertiefungsmodul Bioinformatik (Bachelor Informatik (Studienbeginn vor 01.08.2010)) Vertiefungsmodul Computational Intelligence (Bachelor Informatik) Vertiefungsmodul Geoinformatik (Bachelor Informatik (Studienbeginn vor 01.08.2010)) Vertiefungsmodul Life Science-Informatik (Bachelor Informatik) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | Student assessment will be based on a group project and a written report. Small student groups will implement a system for model-based 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. |
| An-/Abmeldung zur Leistungsüberprüfung | Anmelden: Belegen; Abmelden: Dozierende |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,5 |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch |
| Anbietende Organisationseinheit | Fachbereich Informatik |