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An ASM Fitting Method Based on Machine Learning that Provides a Robust Parameter Initialization for AAM Fitting (bibtex)
@INPROCEEDINGS{wimmer_asm_2008,
  author = {Wimmer, Matthias and Fujie, Shinya and Stulp, Freek and Kobayashi,
	Tetsunori and Radig, Bernd},
  title = {An {ASM} Fitting Method Based on Machine Learning that Provides a
	Robust Parameter Initialization for {AAM} Fitting},
  booktitle = {Proc. of the International Conference on Automatic Face and Gesture
	Recognition ({FGR08)}},
  year = {2008},
  address = {Amsterdam, Netherlands},
  month = sep,
  abstract = {Due to their use of information contained in texture, Active Appearance
	Models ({AAM)} generally outperform Active Shape Models ({ASM)} in
	terms of fitting accuracy. Although many extensions and improvements
	over the original {AAM} have been proposed, on of the main drawbacks
	of {AAMs} remains its dependence on good initial model parameters
	to achieve accurate fitting results. In this paper, we determine
	the initial model parameters for {AAM} fitting with {ASM} fitting,
	and use machine learning techniques to improve the scope and accuracy
	of {ASM} fitting. Combining the precision of {AAM} fitting with the
	large radius of convergence of learned {ASM} fitting improves the
	results by an order of magnitude, as our empirical evaluation on
	a database of publicly available benchmark images demonstrates.}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr