A Vision-Based System For Automatic Detection and Extraction Of Road Networks

In this paper we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory(gabor filtering, tensor voting) and optimized segmentation techniques(global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local presicion of the gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and ...

• Type: Conference
• Year: 2008
• Where: WACV
• Authors: Charalambos Poullis, Suya You, Ulrich Neumann
• Keywords: Computer Vision, Geospatial Features, Perceptual Grouping, Tensor Voting, WACV 2008