Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose using multi-scale Conditional Random Fields to classes 3D outdoor terrestrial laser scanned data.  improved Lim and Suterpsilas methods 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, ldquosuper-voxelsrdquo. 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 (Lim and Suter, 2007). The overall computation time by labeling the super-voxels instead of individual points is lor than the previous 3D data labeling approaches.
['Ee Hui Lim', 'David Suter']
I am writing a research paper on a idea Multi-scale Conditional Random Fields for over-segmented irregular 3D point clouds classification
[{"from": "human", "value": "I am writing a research paper on a idea Multi-scale Conditional Random Fields for over-segmented irregular 3D point clouds classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose using multi-scale Conditional Random Fields to classes 3D outdoor terrestrial laser scanned data.  improved Lim and Suterpsilas methods 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, ldquosuper-voxelsrdquo. 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 (Lim and Suter, 2007). The overall computation time by labeling the super-voxels instead of individual points is lor than the previous 3D data labeling approaches."}]