Automatic orientation and recognition in highly structured scenes1
The paper discusses the impact of scene and assessment models for videometry. The role of geometric and semantic scene knowledge for 3D-modeling is discussed as well as the impact on the 3D-reconstruction process and the accuracy of results. Three issues are covered: the automation of orientation and calibration procedures, the recognition for 3D-mensuration and automatic 3D-reconstruction. Full automation of calibration and orientation procedures appears to be necessary for enlarging the field of applications also for non-specialists. Explicit geometric information and semantic knowledge about the scene are needed to achieve reliable 3D-descriptions. The focus of achieving highest possible accuracy needs to be embedded into a broader context of scene analysis. Models must be able to reflect the different aggregation layers of composite objects to automatically identify object parts in the image data. Examples are given on the role of projective geometry for automatic calibration, the role of scene knowledge for automatic orientation and on traffic light program for automatic evaluation. They demonstrate the feasibility of tools from computer vision for image metrology.
semantic scene knowledge 3d-modeling computer vision and photogrammetry camera orientation object- and image models segmentation