Approximate Envelope Minimization for Curvature Regularity
Abstract
We propose a method for minimizing a non-convex function, which can be split up into a sum of simple functions. The key idea of the method is the approximation of the convex envelopes of the simple functions, which leads to a convex approximation of the original function. A solution is obtained by minimizing this convex approximation. Cost functions, which fulfill such a splitting property are ubiquitous in computer vision, therefore we explain the method based on such a problem, namely the non-convex problem of binary image segmentation based on Euler’s Elastica .
Cite
Text
Heber et al. "Approximate Envelope Minimization for Curvature Regularity." European Conference on Computer Vision Workshops, 2012. doi:10.1007/978-3-642-33885-4_29Markdown
[Heber et al. "Approximate Envelope Minimization for Curvature Regularity." European Conference on Computer Vision Workshops, 2012.](https://mlanthology.org/eccvw/2012/heber2012eccvw-approximate/) doi:10.1007/978-3-642-33885-4_29BibTeX
@inproceedings{heber2012eccvw-approximate,
title = {{Approximate Envelope Minimization for Curvature Regularity}},
author = {Heber, Stefan and Ranftl, René and Pock, Thomas},
booktitle = {European Conference on Computer Vision Workshops},
year = {2012},
pages = {283-292},
doi = {10.1007/978-3-642-33885-4_29},
url = {https://mlanthology.org/eccvw/2012/heber2012eccvw-approximate/}
}