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Automatically Learning the Objective Function for Model Fitting (bibtex)
  author = {Wimmer, Matthias and Radig, Bernd},
  title = {Automatically Learning the Objective Function for Model Fitting},
  booktitle = {Proceedings of the Meeting in Image Recognition and Understanding
  year = {2007},
  address = {Hiroshima, Japan},
  month = jul,
  abstract = {Model-based image interpretation has proven to appropriately extract
	high-level information from images. A priori knowledge about the
	object of interest represents the basis of this task. Model fitting
	determines the model that best matches a given image by searching
	for the global optimum of an objective function. Unfortunately, the
	objective function is usually designed manually, based on implicit
	and domain-dependent knowledge. In contrast, this paper describes
	how to obtain highly accurate objective functions by learning them
	from annotated training images. It automates many critical decisions
	and the remaining manual steps hardly require domain-dependent knowledge
	at all. This approach yields highly accurate objective functions.
	Our evaluation fits a face model to a publicly available image database
	and compares the obtained results to a recent state-of-the-art approach.}
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Last edited 29.01.2013 17:37 by Quirin Lohr