Machine Vision and Applications
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
New Prospects in Line Detection by Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A Comparison of Measures for Detecting Natural Shapes in Cluttered Backgrounds
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic extraction of roads from aerial images based on scale space and snakes
Machine Vision and Applications
A Probabilistic Multi-scale Model for Contour Completion Based on Image Statistics
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
On the Incorporation of shape priors into geometric active contours
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
A Gibbs Point Process for Road Extraction from Remotely Sensed Images
International Journal of Computer Vision
More-Than-Topology-Preserving Flows for Active Contours and Polygons
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Network connectivity via inference over curvature-regularizing line graphs
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
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One of the main difficulties in extracting line networks from images, and in particular road networks from remote sensing images, is the existence of interruptions in the data caused, for example, by occlusions. These can lead to gaps in the extracted network that do not correspond to gaps in the real network. In this paper, we describe a higher-order active contour energy that in addition to favouring network-like regions, includes a prior term penalizing networks containing `nearby opposing extremities', thereby making gaps in the extracted network less likely. The new energy term causes such extremities to attract one another during gradient descent. They thus move towards one another and join, closing the gap. To minimize the energy, we develop specific techniques to handle the high-order derivatives that appear in the gradient descent equation. We present the results of automatic extraction of networks from real remote-sensing images, showing the ability of the model to overcome interruptions.