Probabilistic Morphable Models
June 6th - June 10th, 2017.
A follow-up on the online course on statistical shape modelling
Probabilistic Morphable Models are a fully probabilistic approach to model-based image analysis, based on:
- the theory of Gaussian processes for modelling shapes
- Markov chain Monte Carlo methods for model fitting
- a software framework for efficient development of image analysis solutions.
This course teaches the theoretical basis of shape modelling and model fitting, and provides an introduction on how to use the software framework Scalismo for practial applications in medical image analysis and 2D face image analysis.
The course consists of two parts: The basic theory on shape modelling and practical introduction to Scalismo is given as a open online course and taken individually by the participants. Participants of this course, who submit a solution to the course project, are invited to attend the summer school, which is hosted at the University of Basel. In the summer school, participants will learn about probabilistic methods for model fitting, and can deepen their knowledge on shape modelling in practical projects.
The intended audience for the course are beginning graduate students and researchers in computer vision and medical image analysis, who plan to apply statistical shape models as part of their work.
Prerequisite is good knowledge of basic probability theory and statistica as well as linear algebra. Participants should have basic programming skills in at least one programming language (ideally Java, C++ or Python).