A Fast Line Finder for Vision-Guided Robot Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Building Outline Extraction from Digital Elevation Models Using Marked Point Processes
International Journal of Computer Vision
Automatically Conflating Road Vector Data with Orthoimagery
Geoinformatica
EURASIP Journal on Applied Signal Processing
Recognizing cars in aerial imagery to improve orthophotos
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Automatic extraction of road intersection position, connectivity, and orientations from raster maps
Proceedings of the 16th ACM SIGSPATIAL international conference 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
From GPS traces to a routable road map
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Augmenting cartographic resources for autonomous driving
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Detecting ground shadows in outdoor consumer photographs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Learning to detect roads in high-resolution aerial images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
An unsupervised, online learning framework for moving object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Geometric overpass extraction from vector road data and DSMs
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
CrowdAtlas: self-updating maps for cloud and personal use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Mining large-scale gps streams for connectivity refinement of road maps
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Cartographic databases can be kept up to date through aerial image analysis. Such analysis is optimized when one knows what parts of an aerial image are roads and when one knows locations of complex road structures, such as overpasses and intersections. This paper proposes self-supervised computer vision algorithms that analyze a publicly available cartographic resource (i.e., screenshots of road vectors) to, without human intervention, identify road image-regions and detects overpasses. Our algorithm segments a given input image into two parts: road- and non-road image regions. It does so not by learning a global appearance model of roads from hand-labeled data, but rather by approximating a locally consistent model of the roads' appearance from self-obtained data. In particular, the learned local model is used to execute a binary classification. We then apply an MRF to smooth potentially inconsistent binary classification outputs. To detect overpasses, our method scrutinizes screenshots of road vector images to approximate the geometry of the underlying road vector and use the estimated geometry to localize overpasses. Our methods, based on experiments using inter-city highway ortho-images, show promising results. Segmentation results showed on average over 90% recall; overpass detection results showed 94% accuracy.