An Experimental Comparison of Discrete and Continuous Shape Optimization Methods
Abstract
Shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview reconstruction. In this paper, we focus on a certain class of binary labeling problems which can be globally optimized both in a spatially discrete setting and in a spatially continuous setting. The main contribution of this paper is to present a quantitative comparison of the reconstruction accuracy and computation times which allows to assess some of the strengths and limitations of both approaches. We also present a novel method to approximate length regularity in a graph cut based framework: Instead of using pairwise terms we introduce higher order terms. These allow to represent a more accurate discretization of the L _2-norm in the length term.
Cite
Text
Klodt et al. "An Experimental Comparison of Discrete and Continuous Shape Optimization Methods." European Conference on Computer Vision, 2008. doi:10.1007/978-3-540-88682-2_26Markdown
[Klodt et al. "An Experimental Comparison of Discrete and Continuous Shape Optimization Methods." European Conference on Computer Vision, 2008.](https://mlanthology.org/eccv/2008/klodt2008eccv-experimental/) doi:10.1007/978-3-540-88682-2_26BibTeX
@inproceedings{klodt2008eccv-experimental,
title = {{An Experimental Comparison of Discrete and Continuous Shape Optimization Methods}},
author = {Klodt, Maria and Schoenemann, Thomas and Kolev, Kalin and Schikora, Marek and Cremers, Daniel},
booktitle = {European Conference on Computer Vision},
year = {2008},
pages = {332-345},
doi = {10.1007/978-3-540-88682-2_26},
url = {https://mlanthology.org/eccv/2008/klodt2008eccv-experimental/}
}