Technical University of Munich

@inproceedings{wimmer_learning_2006,
 author = {M Wimmer and F Stulp and S Tschechne and B Radig},
 title = {Learning Robust Objective Functions for Model Fitting in Image Understanding
	Applications},
 booktitle = {Proceedings of the 17th British Machine Vision Conference ({BMVC)}},
 year = {2006},
 editor = {Chantler, Michael J. and Trucco, Emanuel and Fisher, Robert B.},
 volume = {3},
 pages = {1159--1168},
 address = {Edinburgh, {UK}},
 month = {sep},
 publisher = {{BMVA}},
 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.},
}