Extremal feature extraction from 3-D vector and noisy scalar fields

We are interested in feature extraction from volume data in terms of coherent surfaces and 3-D space curves. The input can be an inaccurate scalar or vector field, sampled densely or sparsely on a regular 3-D grid, in which poor resolution and the presence of spurious noisy samples make traditional iso-surface techniques inappropriate. In this paper, we present a general-purpose methodology to extract surfaces or curves from a digital 3-D potential vector field fs; vg, in which each voxel holds a scalar s designating strength, and a vector v indicating direction. For scalar, sparse or low resolution data, we "vectorize"and "densify"the volume by TensorVoting to produce dense vector fields suitable as input to our algorithms, the Extremal Surface and Curve Algorithms. Both algorithms extract, with sub-voxel precision, coherent features representing local extrema in the given vectorfield. Thesecoherentfeatures are a holefree triangulation mesh (in the surface case), ...

• Type: Conference
• Year: 1998
• Where: VISUALIZATION
• Authors: Chi-Keung Tang, Gérard G. Medioni
• Keywords: 3-D Potential Vector, 3-D Space Curves, Vector Fields, Visualization, VISUALIZATION 1998