Please use this identifier to cite or link to this item:
Title: A vision-based system for automatic detection and extraction of road networks
Authors: You, S. 
Neumann, U.
Poullis, Charalambos 
Keywords: Computer networks;Computer vision;Sensor networks
Issue Date: 2008
Publisher: IEEE
Source: Applications of Computer Vision, 2008, Copper Mountain
Abstract: 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 results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and then transforming them to their polygonal representations.
DOI: 10.1109/WACV.2008.4543996
Rights: © IEEE
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Citations 20

checked on May 1, 2018

Page view(s)

Last Week
Last month
checked on Dec 14, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.