Christian Wiedemann

CAD-based recognition of 3D objects in monocular images

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

Combining Scale-Space and Similarity-Based Aspect Graphs for Fast 3D Object Recognition

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 objects 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.

Recognition and Tracking of 3D Objects

This paper describes a method for recognizing and tracking 3D objects in a single camera image and for determining their 3D poses. A model is trained solely based on the geometry information of a 3D CAD model of the object. We do not rely on texture or reflectance information of the objects surface, making this approach useful for a wide range of object types and complementary to descriptor-based approaches. An exhaustive search, which ensures that the globally best matches are always found, is combined with an efficient hierarchical search, a high percentage of which can be computed offline, making our method suitable even for time-critical applications. The method is especially suited for, but not limited to, the recognition and tracking of untextured objects like metal parts, which are often used in industrial environments.

Increasing Efficiency of Road Extraction by Self-Diagnosis

The aim of self-diagnosis (internal evaluation) is to determine the geometric and semantic accuracy of the extracted objects during the extraction process. This article presents general ideas on the methodology, implementation, and representation of self-diagnosis within automatic object extraction systems. The authors demonstrate how results attached with confidence values can increase system efficiency for practical applications. They show the potential of self-diagnosis by two road extraction approaches, based on a test series of aerial images, that have been extended by a self-diagnosis component. The authors note that, in order to speed up the time- and cost-intensive inspection, the system should provide the operator with confidence values characterizing its own performance. In practice, however, this is rarely the case. The authors conclude that self-diagnosis is a very helpful tool to guide the user through the road network by pointing to such parts where problems or uncertainties occurred during the extraction. The self-diagnosis also shows great potential to improve an automatic extraction, mainly due to less sensitivity against improper parameter settings. However, there are some deficiencies in distinguishing between erroneous and uncertain, but correct, extractions.

The role of context for road extraction from SAR imagery

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.

A scheme for road extraction in rural areas and its evaluation

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.

Acquisition and updating of ATKIS using satellite remote sensing imagery

Remote sensing is one of the workhorses of topographic and thematic mapping of large areas of the Earth surface and is a prime data source for the acquisition and updating of topographic geo-data at small scales. Satellite imagery of a sub-meter ground resolution in the panchromatic channel, which has been announced for some time and will most probably be available in the near future, has the potential to be also useful in medium scale applications. Along with the better resolution a change of automatic image exploitation techniques from purely pixel-based multi-spectral classification to model-based image analysis, and an increasing integration with GIS (geographic information system) can be observed.