Learning

People:

Abstract:

Learning is one of the most important parts in almost any recognition framework. Although there are a lot of well-studied and well-functioning learning techniques, it contains a number of extremely complicated and yet not solved (or even not formulated properly) mathematical problems. This especially concerns the area of structural learning, where objects consisting of several interconnected parts are investigated. We are working on improving existing approaches and providing new ones to the structural learning framework.

Learning of Expert Performance, Contour Shapes and Graphical Model Parameters

We deal with two learning problems. The first one concerns a statistical method for assessing the error rates of experts in case of ground-truth absence. The second problem addresses a learning approach for graphical models in situations where an inference is hard. more

Convenient Learning

We focus on semi-automatic image analysis techniques which can be adapted to new tasks by a small amount of user input. To this end an interactive learning and segmentation user interface has been developed to facilitate fast and interactive exploration of images without the need of custom programming and image processing expertise. The main goal is to provide a software tool for interactive exploration and segmentation of image data covering a wide range of biomedical and industrial use cases. Offering effective classifiers, feature selection, and the support of up to three-dimensional multi-spectral data are major claims of this work. We enable the user to label objects of interest via an easy-to-use mouse interface. In addition, we remove the restriction to two classes (foreground and background) and allow for any number, as needed by the task at hand.