Publications
• Sorted by Date • Classified by Publication Type • Classified by Research Category •
Matthias Wimmer, Sylvia Pietzsch, Freek Stulp, and Bernd Radig. Learning Robust Objective Functions with Application to Face Model Fitting. In Proceedings of the 29th Annual Symposium of the German Association for Pattern Recognition (DAGM), 2007.
Download
Abstract
Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge is to determine the model parameters that best match a given image by searching for the global optimum of the involved objective function. Unfortunately, this function is usually designed manually, based on implicit and domain-dependent knowledge, which prevents the fitting task from yielding accurate results. In this paper, we demonstrate how to improve model fitting by learning objective functions from annotated training images. Our approach automates many critical decisions and the remaining manual steps hardly require domain-dependent knowledge. This yields more robust objective functions that are able to achieve the accurate model fit. Our evaluation uses a publicly available image database and compares the obtained results to a recent state-of-the-art approach.
BibTeX Entry
@InProceedings{wimmer07learningrobust,
author = {Matthias Wimmer and Sylvia Pietzsch and Freek Stulp and Bernd Radig},
title = {Learning Robust Objective Functions with Application to Face Model Fitting},
booktitle = {Proceedings of the 29th Annual Symposium of the German Association for Pattern Recognition (DAGM)},
year = {2007},
bib2html_pubtype = {Refereed Conference Paper},
bib2html_rescat = {Image Understanding - Face Model Fitting},
abstract = {
Model-based image interpretation extracts high-level information from images
using a priori knowledge about the object of interest. The computational
challenge is to determine the model parameters that best match a given image
by searching for the global optimum of the involved objective function.
Unfortunately, this function is usually designed manually, based on implicit
and domain-dependent knowledge, which prevents the fitting task from yielding
accurate results.
In this paper, we demonstrate how to improve model fitting by learning
objective functions from annotated training images. Our approach automates
many critical decisions and the remaining manual steps hardly require
domain-dependent knowledge. This yields more robust objective functions that
are able to achieve the accurate model fit. Our evaluation uses a publicly
available image database and compares the obtained results to a recent
state-of-the-art approach.
}
}
Generated by bib2html.pl (written by Patrick Riley ) on Tue Sep 23, 2008 15:09:11 UTC

















