A Fast Line Finder for Vision-Guided Robot Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-Cover Approach for Finding Salient Curves
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Automatically Conflating Road Vector Data with Orthoimagery
Geoinformatica
Recognizing cars in aerial imagery to improve orthophotos
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Detecting ground shadows in outdoor consumer photographs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Geometric overpass extraction from vector road data and DSMs
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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This paper presents new aerial image analysis algorithms that, from highway ortho-images, produce lane-level detailed maps. We analyze screenshots of road vectors to obtain the relevant spatial and photometric cues of road image-regions. We then refine the obtained patterns to generate hypotheses about the true road-lanes. A road-lane hypothesis, since it explains only a part of the true road-lane, is then linked to other hypotheses to completely delineate boundaries of the true road-lanes. Finally, some of the refined image cues about the underlying road network are used to guide a linking process of road-lane hypotheses. We tested the accuracy and robustness of our algorithms with high-resolution, inter-city highway ortho-images. Experimental results show promise in producing lane-level detailed highway maps from ortho-image analysis -- 89% of the true road-lane boundary pixels were successfully detected and 337 out of 417 true road-lanes were correctly recovered.