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An ASM Fitting Method Based on Machine Learning that Provides a Robust Parameter Initialization for AAM Fitting (bibtex) [pdf]
@inproceedings{ wimmer08anasmfittingmethod,
  author    		= {Matthias Wimmer and Shinya Fujie and Freek Stulp and Tetsunori Kobayashi and Bernd Radig},
  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},
  month			= {September},
  address   		= {Amsterdam, Netherlands},
  xxxvolume		= {},
  xxxpages		= {},
  bib2html_pubtype	= {Refereed Conference Paper},
  bib2html_rescat   	= {Image Understanding},
  bib2html_groups   	= {IU},
  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 09.03.2013 19:45 by goron