Skeleton Graph Generation for Feature Shape Description

In our scheme, a PPE (the PowerPC processor in the Cell B.E.) is responsi- ble for traversing a hierarchical data structure and generat- ing the lists of intersecting voxels along

The skeletonisation algorithm proposed in this paper consists of three steps: (i) extraction of a graph from an octree organization, (ii) reduction of the graph to the skeleton

In order to overcome these limitations, we in- troduce a volumetric primitive, that we call split-voxel, which replaces blocks of N 3 voxels by one single voxel that is split by

The values in this case represent accurate occlusion factors, which decrease as voxels come closer to occluders (features). Because regions are now overlapping, we have to define a

voxel confidence measure α i ∈ [0 : 1] is introduced. It is based on the Euclidean distance from the voxel centre to the nearest input point d i E. Confidence values in voxels

Modification of Classification Applying the probabilistic classifier to the histogram assigns class membership proba- bilities to all voxels of the data set, which can be modified

The Extended Reeb graph (ERG) is a 3D shape descriptor that fulfils the graph requirements on G of being an undi- rected and labelled graph. Like other methods based on the Reeb

The final segmentation results are improved by tackling two disadvantages in graph construction of traditional models: graph structure modeling and graph edge weights formation1.