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2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-7
Ee Hui Lim , Institute for Vision System Engineering, Monash University, Clayton VIC Australia
David Suter , Institute for Vision System Engineering, Monash University, Clayton VIC Australia
ABSTRACT
In this paper, we propose using multi-scale Conditional Random Fields to classes 3D outdoor terrestrial laser scanned data. We improved Lim and Suter’s methods [1] by introducing regional edge potentials in addition to the local edge and node potentials in the multi-scale Conditional Random Fields, and only a relatively small amount of increment in the computation time is required to achieve the improved recognition rate. In the model, the raw data points are over-segmented into an improved mid-level representation, “super-voxels”. Local and regional features are then extracted from the super-voxel and parameters learnt by the multi-scale Conditional Random Fields. The classification accuracy is improved by 5% to 10% with our proposed model compared to labeling with Conditional Random Fields in [1]. The overall computation time by labeling the super-voxels instead of individual points is lower than the previous 3D data labeling approaches.
INDEX TERMS
CITATION
Ee Hui Lim, David Suter, "Multi-scale Conditional Random Fields for over-segmented irregular 3D point clouds classification", 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 00, no. , pp. 1-7, 2008, doi:10.1109/CVPRW.2008.4563064
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