Dirk Stallmann

High-accuracy edge-matching with an extension of the MPGC-matching algorithm

In industrial dimensional inspection and quality control there is an increasing need for fast and automatic high-accuracy measurement systems. For vision systems to match these requirements all system components have to be tuned carefully. A key role in such a system is played by the measurement algorithm. This paper demonstrates how the area-based multi- photo geometrical constrained (MPGC) matching algorithm can be modified for the highly accurate measurement of object edges. It can be expected that this algorithm allows the measurement of nontargeted but well-defined object features with a relative accuracy of 1:25000.

Algorithms for Automated Extractioin of Man-Made Objects from Raster Image Data in GIS

This paper addresses the topic of semi-automatic object extraction for GIS data capture. It includes novel algorithms and conceptual strategies, together with performed experiments, encountered problems and adopted solutions. In particular, we present an algorithm and obtained results for semi-automatic extraction of road networks from SPOT imagery using wavelet-transformed images and cost functions expressing local gray value variations and global continuity constraints. For larger scale images and for various object types, we present our technique of least squares template matching for edge extraction, which uses local gray value variations to precisely identify edge locations. Finally, we propose a global approach for semi-automatic object outline detection, whereby least squares matching provides the mathematical foundation, while global continuity is enforced through the introduction of object-type-dependent shape constraints.

Trinocular vision for automatic and robust 3D determination of the trajectories of moving objects

An automatic procedure for tracking signalised points on multiple, differently moving surfaces will be described. The practical tests involve tracking of points on a person walking within a calibration frame. Three simultaneously acquiring CCD cameras, which are oriented using control points, provide the input data. For implementation, the hardware (H/W) and software (S/W) of a digital photogrammetric system have been used. The procedure includes extraction of the signalised points and finding corresponding points in all three images, thus providing 2-D and 3-D coordinates and tracking using the information in both image and object space. Reliability aspects such as occlusions, multiple solutions and different backgrounds will be analysed. The effect of a third image on reliability will be analysed.