Extremal feature extraction from 3-D vector and noisy scalar fields
Proceedings of the conference on Visualization '98
Using an Individual Evolution Strategy for Stereovision
Genetic Programming and Evolvable Machines
Dynamic flies: a new pattern recognition tool applied to stereo sequence processing
Pattern Recognition Letters
Inferring Segmented Surface Description from Stereo Data
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A clustering approach to free form surface reconstruction from multi-view range images
Image and Vision Computing
Mobile robot sensor fusion using flies
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
| Hi-index | 0.00 |
We are interested in descriptions of 3-D data sets, as obtained from stereo or a 3-D digitizer. We therefore consider as input a sparse set of points, possibly associated with orientation information. In this paper, we address the problem of inferring integrated high-level descriptionssuch as surfaces, curves, and junctions from a sparse point set. While the method described in [5], [6] provides excellent results for smooth structures, it only detects discontinuities, but does not localize them. For precise localization, we propose a non-iterative cooperative algorithm in which surfaces, curves, and junctions work together: Initial estimates are computed based on [5], [6], where each point in the given sparse and possibly noisy point set is convolved with a predefined vector mask to produce dense saliency maps. These maps serve as input to our novel maximal surface and curve marching algorithms for initial surface and curve extraction. Refinement of initial estimates is achieved by hybrid voting using excitatory and inhibitory fields for inferring reliable and natural extension so that surface/curve and curve/junction discontinuities are preserved. Results on several synthetic as well as real data sets are presented.