Volkmar Welker

Non-convex polyhedral volume of interest selection

We introduce a novel approach to specify and edit volumes of interest (VOI for short) interactively. Enhancing the capabilities of standard systems we provide tools to edit the VOI by defining a not necessarily convex polyhedral bounding object. We suggest to use low-level editing interactions for moving, inserting and deleting vertices, edges and faces of the polyhedron. The low-level operations can be used as building blocks for more complex higher order operations fitting the application demands. Flexible initialization allows the user to select within a few clicks convex VOI that in the classical clipping plane model need the specification of a large number of cutting planes. In our model it is similarly simple to select non-convex VOI. Boolean combinations allow to select non-connected VOI of arbitrary complexity. The polyhedral VOI selection technique enables the user to define VOI with complex boundary structure interactively, in an easy to comprehend and predictable manner.

Localization and classification based on projections

Due to the loss of range information, projections as input data for a 3-D object recognition algorithm are expected to increase the computational complexity. In this work, however, we demonstrate that this deficiency carries potential for complexity reduction of major vision problems. We show that projections provide a reduction of feature dimensions, and lead to structures exhibiting simple combinatorial properties. The theoretical framework is embedded in a probabilistic setting which deals with uncertainties and variations of observed features. In statistics marginal densities and the assumption of independency prove to be the key tools when one encounters projections. The examples discussed in this paper include feature matching, pose estimation as well as classification of 3-D objects. The final experimental evaluation demonstrates the practical importance of the marginalization concept and independency assumptions.