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An approach for the automatic extraction of roads ftom digital aeria)-imagery is proposed. It makes use of several versions of the saie aeriaT image with differcnt reso-lutions' Roads ate modeled as a network of intersections and links between these intersections, and are found by a grouping prccess. The context of roads is hierarchically structured into a(More)
We propose a new approach for automatic road extraction from aerial imagery with a model and a strategy mainly based on the multi-scale detection of roads in combination with geometry-constrained edge extraction using snakes. A main advantage of our approach is, that it allows for the first time a bridging of shadows and partially occluded areas using the(More)
In this paper, we present work on automatic road extraction from high resolution aerial imagery taken over urban areas. In order to deal with the high complexity of this type of scenes, we integrate detailed knowledge about roads and their context using explicitly formulated scale-dependent models. The knowledge about how and when certain parts of the road(More)
This paper presents a multi-resolution approach for automatic extraction of roads from digital aerial imagery. Roads are modeled as a network of intersections and links between the intersections. of the image into context regions is done by texture analysis. The approach to detect roads is based on the extraction of edges in a high resolution image and the(More)
This paper proposes an approach for automatic road extraction in aerial imagery which exploits the scale-space behavior of roads in combination with geometric constrained snake-based edge extraction. The approach not only has few parameters to be adjusted, but for the first time allows for a bridging of shadows and partially occluded areas using the heavily(More)
This paper approaches the problem of road extraction from three different directions. The first is the use of multiple scales. This combines detailed information of fine scale, like the markings, with abstract information of coarse scale, like the road network. The second direction is the extension of the multi-scale modeling with the context, i.e., the(More)
An approach for real-time object recognition in digital images based on the principle of the generalized Hough transform is proposed. It combines robustness against occlusions, distortions, and noise with invariance under rigid motion and local illumination changes. The computational effort is reduced by employing a novel efficient limitation of the search(More)