Research Training Group (RTG) 1653
Spatio/Temporal Graphical Models and Applications in Image Analysis

Probabilistic graphical models provide a consistent framework for the statistical modeling and the computational analysis of scientific empirical data. The past decade has witnessed a significant increase in respective research in the field of image analysis and related application areas, driven by the synergy between statistics, pattern recognition, computer vision and machine learning. The objective is to devise models that enable to infer a coherent global interpretation of noisy and ambiguous local image measurements, taking into account spatiotemporal context in images and videos, and domain-specific contextual knowledge.

Applications of probabilistic graphical models to such large-scale problems raise numerous research problems of modeling and algorithm design for inference and learning, requiring interdisciplinary expertise in applied mathematics, computer science and physics, besides a profound knowledge of the respective application areas.

The basic intention of the Research Training Group is to gather experts from these fields and to establish a coherent research and study program on probabilistic graphical models, with a focus on spatial and spatiotemporal models and their applications in image analysis. The project treats methodological basic research on an equal footing with challenging scientific applications of image analysis in environmental science, life sciences and industry.

The Research Training Group will provide a scientifically unique environment for study, collaboration and innovative research on probabilistic graphical models across disciplines, producing highly-qualified candidates for research careers in academia and industry.