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2007
ACM

Linear feature extraction using perceptual grouping and graph-cuts

10 years 11 days ago
Linear feature extraction using perceptual grouping and graph-cuts
In this paper we present a novel system for the detection and extraction of road map information from high-resolution satellite imagery. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (gabor filtering, tensor voting) and segmentation (graph-cuts) into a unified framework to address the problems of road feature detection and classification. Local orientation information is derived using a bank of gabor filters and is refined using tensor voting. A segmentation method based on global optimization by graph-cuts is developed for segmenting foreground(road pixels) and background objects while preserving oriented boundaries. Road centerlines are detected using pairs of gaussian-based filters and road network vector maps are finally extracted using a tracking algorithm. The proposed system works with a single or multiple images, and any available elevation information. User interaction is limited and is performed at the begining of...
Charalambos Poullis, Suya You, Ulrich Neumann
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2007
Where GIS
Authors Charalambos Poullis, Suya You, Ulrich Neumann
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