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Learning Robust Objective Functions for Model Fitting in Image Understanding Applications (bibtex) [pdf]
  author 			= {Matthias Wimmer and Freek Stulp and Stephan Tschechne and Bernd Radig},
  title 			= {Learning Robust Objective Functions for Model Fitting in Image Understanding Applications},
  booktitle 		= {Proceedings of the 17th~British Machine Vision Conference~(BMVC)},
  editor    		= {Michael J. Chantler and Emanuel Trucco and Robert B. Fisher},
  year 				= {2006},
  month     		= {September},
  publisher 		= {BMVA},
  address   		= {Edinburgh, UK},
  volume    		= {3},
  pages     		= {1159-1168},
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Image Understanding},
  bib2html_groups   = {IU},
  abstract 			= 
  						{Model-based methods in computer vision have proven to be a good approach for compressing the large amount of information in images. Fitting algorithms search for those parameters of the model that optimise the objective function given a certain image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed ad hoc and heuristically with much implicit domain-dependent knowledge.

						This paper formulates a set of requirements that robust objective functions should satisfy. Furthermore, we propose a novel approach that learns the objective function from training images that have been annotated with the preferred model parameters. The requirements are automatically enforced during the learning phase, which yields generally applicable objective functions. We compare the performance of our approach to other approaches. For this purpose, we propose a set of indicators that evaluate how well an objective function meets the stated requirements.}
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Last edited 09.03.2013 19:45 by goron