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Enabling Users to Guide the Design of Robust Model Fitting Algorithms (bibtex)
@INPROCEEDINGS{wimmer_enabling_2007,
  author = {Wimmer, Matthias and Stulp, Freek and Radig, Bernd},
  title = {Enabling Users to Guide the Design of Robust Model Fitting Algorithms},
  booktitle = {Workshop on Interactive Computer Vision, held in conjunction with
	{ICCV} 2007},
  year = {2007},
  pages = {28},
  address = {Rio de Janeiro, Brazil},
  month = oct,
  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.},
  isbn = {978-1-4244-1631-8}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr