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Matthias Wimmer, Freek Stulp, and Bernd Radig. Enabling Users to Guide the Design of Robust Model Fitting Algorithms. In Workshop on Interactive Computer Vision (ICV 2007), pp. 28, Omnipress, Rio de Janeiro, Brazil, October 2007.
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Abstract
Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge in model fitting is to determine the model parameters that best match a given image, which corresponds to finding the global optimum of the objective function. When it comes to the robustness and accuracy of fitting models to specific images, humans still outperform state-of-the-art model fitting systems. Therefore, we propose a method in which non-experts can guide the process of designing model fitting algorithms. In particular, this paper demonstrates how to obtain robust objective functions for face model fitting applications, by learning their calculation rules from example images annotated by humans. We evaluate the obtained function using a publicly available image database and compare it to a recent state-of-the-art approach in terms of accuracy.
BibTeX Entry
@inproceedings{ wimmer07enabling,
author = {Matthias Wimmer and Freek Stulp and Bernd Radig},
title = {Enabling Users to Guide the Design of Robust Model Fitting Algorithms},
booktitle = {Workshop on Interactive Computer Vision~(ICV~2007)},
year = {2007},
month = {October},
address = {Rio de Janeiro, Brazil},
pages = {28},
bib2html_pubtype = {Refereed Workshop Paper},
bib2html_rescat = {Image Understanding},
bib2html_groups = {IU},
ISBN = {978-1-4244-1631-8},
ISSN = {1550-5499},
publisher = {Omnipress},
abstract = {Model-based image interpretation extracts high-level information
from images using a priori knowledge about the
object of interest. The computational challenge in model fitting
is to determine the model parameters that best match a
given image, which corresponds to finding the global optimum
of the objective function.
When it comes to the robustness and accuracy of fitting
models to specific images, humans still outperform state-of-the-art model fitting systems. Therefore, we propose a
method in which non-experts can guide the process of designing
model fitting algorithms. In particular, this paper
demonstrates how to obtain robust objective functions for
face model fitting applications, by learning their calculation
rules from example images annotated by humans. We
evaluate the obtained function using a publicly available
image database and compare it to a recent state-of-the-art
approach in terms of accuracy.},
}
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