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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Multi-scale Conditional Random Fields for over-segmented irregular 3D point clouds classification
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
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
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.
Citation:
Ee Hui Lim, David Suter, "Multi-scale Conditional Random Fields for over-segmented irregular 3D point clouds classification," cvprw, pp.1-7, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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