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Enabling Users to Guide the Design of Robust Model Fitting Algorithms (bibtex) [pdf]
@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, held in conjunction with ICCV~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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Last edited 09.03.2013 19:45 by goron