Add to ORKGWe explore a novel algorithm to analyze arbitrary distributions of 3D-points. Using a direct tensor field visualization technique allows to easily identify regions of linear, planar or isotropic structure. This approach is very suitable for visual data mining and exemplified upon geoscience applications. It allows to distinguish, for example, power lines and flat terrains in LIDAR scans. We furthermore present the work on the optimization of the computationally intensive algorithm using OpenCL and potentially utilizing the Insieme optimizing compiler framework
The challenge of tensor field visualization is to provide simple and comprehensible representations ...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
Many computer vision systems depend on reliable detection of 3-D boundaries and regions in order to ...
We explore a novel algorithm to analyze arbitrary distributions of 3D-points. Using a direct tensor ...
AbstractBig data in observational and computational sciences impose increasing challenges on data an...
We present an investigation on the use of Tensor Voting for categorizing LIDAR data into outliers, l...
This paper proposes a building façade contouring method from LiDAR (Light Detection and Ranging) sca...
The theme of this thesis is to complete the 3D tensor voting theory for computer vision and graphics...
The point cloud is an unorganized set of points with 3D coordinates (x, y, z) which represents a rea...
Recently, a computational framework for feature extraction and segmentation, Tensor Voting, has bee...
The common statistical methods for supervised classification usually require a large amount of train...
Colocation mining is one of the major spatial data mining tasks. When discovering colocation pattern...
The physical interpretation of mathematical features of tensor fields is highly application-specific...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
Using Light Detection And Radar (LiDAR) large parts of the earth's geography can be captured an repr...
The challenge of tensor field visualization is to provide simple and comprehensible representations ...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
Many computer vision systems depend on reliable detection of 3-D boundaries and regions in order to ...
We explore a novel algorithm to analyze arbitrary distributions of 3D-points. Using a direct tensor ...
AbstractBig data in observational and computational sciences impose increasing challenges on data an...
We present an investigation on the use of Tensor Voting for categorizing LIDAR data into outliers, l...
This paper proposes a building façade contouring method from LiDAR (Light Detection and Ranging) sca...
The theme of this thesis is to complete the 3D tensor voting theory for computer vision and graphics...
The point cloud is an unorganized set of points with 3D coordinates (x, y, z) which represents a rea...
Recently, a computational framework for feature extraction and segmentation, Tensor Voting, has bee...
The common statistical methods for supervised classification usually require a large amount of train...
Colocation mining is one of the major spatial data mining tasks. When discovering colocation pattern...
The physical interpretation of mathematical features of tensor fields is highly application-specific...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
Using Light Detection And Radar (LiDAR) large parts of the earth's geography can be captured an repr...
The challenge of tensor field visualization is to provide simple and comprehensible representations ...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
Many computer vision systems depend on reliable detection of 3-D boundaries and regions in order to ...