Completion Energies and Scale
Abstract
The detection of smooth curves in images and their completion over gaps are two important problems in perceptual grouping. In this paper we examine the notion of completion energy and introduce a fast method to compute the most likely completions in images. Specifically we develop two novel analytic approximations to the curve of least energy. In addition, we introduce a fast numerical method to compute the curve of least energy, and show that our approximations are obtained at early stages of this numerical computation. We then use our newly developed energies to find the most likely completions in images through a generalized summation of induction fields. Since in practice edge elements are obtained by applying filters of certain widths and lengths to the image, we adjust our computation to take these parameters into account. Finally, we show that, due to the smoothness of the kernel of summation, the process of summing induction fields can be run in time that is linear in the number of different edge elements in the image, or in O(N log N) where N is the number of pixels in the image, using multigrid methods.
Cite
Text
Sharon et al. "Completion Energies and Scale." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997. doi:10.1109/CVPR.1997.609432Markdown
[Sharon et al. "Completion Energies and Scale." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997.](https://mlanthology.org/cvpr/1997/sharon1997cvpr-completion/) doi:10.1109/CVPR.1997.609432BibTeX
@inproceedings{sharon1997cvpr-completion,
title = {{Completion Energies and Scale}},
author = {Sharon, Eitan and Brandt, Achi and Basri, Ronen},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1997},
pages = {884-890},
doi = {10.1109/CVPR.1997.609432},
url = {https://mlanthology.org/cvpr/1997/sharon1997cvpr-completion/}
}