Recognizing cars in aerial imagery to improve orthophotos
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
3D Urban Scene Modeling Integrating Recognition and Reconstruction
International Journal of Computer Vision
Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention
International Journal of Robotics Research
Semantic classification in aerial imagery by integrating appearance and height information
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Object Detection using Geometrical Context Feedback
International Journal of Computer Vision
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3D city modeling using computer vision is very challenging. A typical city contains objects which are a nightmare for some vision algorithms, while other algorithms have been designed to identify exactly these parts but, in their turn, suffer from other weaknesses which limit their application. For instance, moving cars with metallic surfaces can degrade the results of a 3D city reconstruction algorithm which is primarily based on the assumption of a static scene with diffuse reflection properties. On the other hand, a specialized object recognition algorithm could be able to detect cars, but also yields too many false positives without the availability of additional scene knowledge. In this paper, the design of a cognitive loop which intertwines both aforementioned algorithms is demonstrated for 3D city modeling, proving that the whole can be much more than the simple sum of its parts. A cognitive loop is the mutual transfer of higher knowledge between algorithms, which enables the combination of algorithms to overcome the weaknesses of any single algorithm. We demonstrate the promise of this approach on a real-world city modeling task using video data recorded by a survey vehicle. Our results show that the cognitive combination of algorithms delivers convincing city models which improve upon the degree of realism that is possible from a purely reconstruction-based approach.