This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.

An approach to intercultural training of nonverbal behavior is presented that draws from research on role-plays with virtual agents and ideas from situated learning. To this end, a mobile serious game is realized where the user acquires knowledge about German emblematic gestures and tries them out in role-plays with virtual agents. Gesture performance is evaluated making use of build-in acceleration sensors of smart phones. After an account of the theoretical background covering diverse areas like virtual agents, situated learning and intercultural training, the paper presents the GAME approach along with details on the gesture recognition and content authoring. By its experience-based role-plays with virtual characters, GAME brings together ideas from situated learning and intercultural training in an integrated approach and paves the way for new m-learning concepts.

This paper provides a method for recognizing 3D objects in a single camera image and for determining their 3D poses.

This paper deals with automatic road extraction from airborne SAR imagery. Automatic extracted road networks are often incomplete. Even sophisticated road extraction approaches that are developed for optical imagery often fail to extract a complete road network. In SAR imagery in contrary to optical imagery, roads are more affected by high backscattering context objects. These local context objects like trees, bridges, or vehicles can disturb road extraction, but they can also support it. We show that an explicit modeling of context objects, leads to improved extraction results.

We propose a scheme for road extraction in rural areas that integrates three different modules with specific strengths. The first module employs local grouping and uses multiple scales and contest to reliably extract most parts of the road network. To connect these parts, the second module exploits the network characteristics of roads for global grouping. The third module completes the network based on an analysis of path lengths within and between connected components of the network. An evaluation of the results shows that the system benefits from the integration of different types of knowledge within the road extraction scheme.