Fast Parameter Sensitivity Analysis of PDE-Based Image Processing Methods
Abstract
We present a fast parameter sensitivity analysis by combining recent developments from uncertainty quantification with image processing operators. The approach is not based on a sampling strategy, instead we combine the polynomial chaos expansion and stochastic finite elements with PDE-based image processing operators. With our approach and a moderate number of parameters in the models the full sensitivity analysis is obtained at the cost of a few Monte Carlo runs. To demonstrate the efficiency and simplicity of the approach we show a parameter sensitivity analysis for Perona-Malik diffusion, random walker and Ambrosio-Tortorelli segmentation, and discontinuity-preserving optical flow computation.
Cite
Text
Pätz and Preusser. "Fast Parameter Sensitivity Analysis of PDE-Based Image Processing Methods." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33786-4_11Markdown
[Pätz and Preusser. "Fast Parameter Sensitivity Analysis of PDE-Based Image Processing Methods." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/patz2012eccv-fast/) doi:10.1007/978-3-642-33786-4_11BibTeX
@inproceedings{patz2012eccv-fast,
title = {{Fast Parameter Sensitivity Analysis of PDE-Based Image Processing Methods}},
author = {Pätz, Torben and Preusser, Tobias},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {140-153},
doi = {10.1007/978-3-642-33786-4_11},
url = {https://mlanthology.org/eccv/2012/patz2012eccv-fast/}
}