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Estimating Natural Activity by Fitting 3D Models via Learned Objective Functions (bibtex)
@INPROCEEDINGS{wimmer_estimating_2007,
  author = {Wimmer, Matthias and Mayer, Christoph and Stulp, Freek and Radig,
	Bernd},
  title = {Estimating Natural Activity by Fitting {3D} Models via Learned Objective
	Functions},
  booktitle = {Workshop on Vision, Modeling, and Visualization ({VMV)}},
  year = {2007},
  volume = {1},
  pages = {233--241},
  address = {Saarbr├╝cken, Germany},
  month = nov,
  abstract = {Model-based image interpretation has proven to robustly extract high-level
	scene descriptors from raw image data. Furthermore, geometric texture
	models represent a fundamental component for visualizing real-world
	scenarios. However, the motion of the model and the real-world object
	must be similar in order to portray natural activity. Again, this
	information can be determined by inspecting images via model-based
	image interpretation. This paper sketches the challenge of fitting
	models to images, describes the shortcomings of current approaches
	and proposes a technique based on machine learning techniques. We
	identify the objective function as a crucial component for fitting
	models to images. Furthermore, we state preferable properties of
	these functions and we propose to learn such a function from manually
	annotated example images.}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr