Confidence and Curvature Estimation of Curvilinear Structures in 3-D
Abstract
In this paper we present a new method for estimating confidence and curvature of 3-D curvilinear structures. The gradient structure tensor (GST) models shift-invariance. The eigenstructure of the tensor allows estimation of local dimensionality, orientation, and the corresponding confidence value. Local rotational invariance, which occurs often in images, causes a lower confidence estimate. This underestimation can be corrected for by a parabolic deformation of the data, in such a way that it becomes translational invariant. We show that the optimal deformation can be found analytically and yields a local curvature estimate as a valuable by-product. We tested our new method on synthetic images and applied it to the detection of channels in 3-D seismic delta.
Cite
Text
Bakker et al. "Confidence and Curvature Estimation of Curvilinear Structures in 3-D." IEEE/CVF International Conference on Computer Vision, 2001. doi:10.1109/ICCV.2001.937616Markdown
[Bakker et al. "Confidence and Curvature Estimation of Curvilinear Structures in 3-D." IEEE/CVF International Conference on Computer Vision, 2001.](https://mlanthology.org/iccv/2001/bakker2001iccv-confidence/) doi:10.1109/ICCV.2001.937616BibTeX
@inproceedings{bakker2001iccv-confidence,
title = {{Confidence and Curvature Estimation of Curvilinear Structures in 3-D}},
author = {Bakker, Peter and van Vliet, Lucas J. and Verbeek, Piet W.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2001},
pages = {139-144},
doi = {10.1109/ICCV.2001.937616},
url = {https://mlanthology.org/iccv/2001/bakker2001iccv-confidence/}
}