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Matthias Wimmer, Freek Stulp, Stephan Tschechne, and Bernd Radig. Learning Robust Objective Functions for Model Fitting in
Image Understanding Applications. In Proceedings of the British Machine Vision Conference (BMVC 2006), pp. 1159 –
1168, BMVA, September 2006.
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[PDF]1.8MB
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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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@InProceedings{wimmer06learning,
title = {Learning Robust Objective Functions for Model Fitting in Image Understanding Applications},
author = {Matthias Wimmer and Freek Stulp and Stephan Tschechne and Bernd Radig},
booktitle = {Proceedings of the British Machine Vision Conference (BMVC 2006)},
year = {2006},
editor = {Michael J. Chantler and Emanuel Trucco and Robert B. Fisher},
month = {September},
pages = {1159 -- 1168},
publisher = {BMVA},
volume = {3},
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. },
bib2html_pubtype = {Refereed Conference Paper},
bib2html_rescat = {Learning Objective Functions for Face Model Fitting}
}
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