A systematic way for region-based image segmentation based on Markov Random Field model
Pattern Recognition Letters
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Saliency of Watershed Contours and Hierarchical Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watershed-based segmentation and region merging
Computer Vision and Image Understanding
Automatic extraction of roads from aerial images based on scale space and snakes
Machine Vision and Applications
A Comparison of Algorithms for Connected Set Openings and Closings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Curvilinear Structures: A Differential Geometric Approach
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Image segmentation and analysis via multiscale gradient watershed hierarchies
IEEE Transactions on Image Processing
A simple unsupervised MRF model based image segmentation approach
IEEE Transactions on Image Processing
EURASIP Journal on Advances in Signal Processing
International Journal of Computer Applications in Technology
Exploiting publicly available cartographic resources for aerial image analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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We present a fast method for road network extraction in satellite images. It can be seen as a transposition of the segmentation scheme "watershed transform + region adjacency graph + Markov random fields" to the extraction of curvilinear objects. Many road extractors which are composed of two stages can be found in the literature. The first one acts like a filter that can decide from a local analysis, at every image point, if there is a road or not. The second stage aims at obtaining the road network structure. In the method, we propose to rely on a "potential" image, that is, unstructured image data that can be derived from any road extractor filter. In such a potential image, the value assigned to a point is a measure of its likelihood to be located in the middle of a road. A filtering step applied on the potential image relies on the area closing operator followed by the watershed transform to obtain a connected line which encloses the road network. Then a graph describing adjacency relationships between watershed lines is built. Defining Markov random fields upon this graph, associated with an energetic model of road networks, leads to the expression of road network extraction as a global energy minimization problem. This method can easily be adapted to other image processing fields, where the recognition of curvilinear structures is involved.