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Estimating Natural Activity by Fitting 3D Models via Learned Objective Functions (bibtex) [pdf]
@inproceedings{ wimmer07estimating,
  author    		= {Matthias Wimmer and Christoph Mayer and Freek Stulp and Bernd Radig},
  title     		= {Estimating Natural Activity by Fitting {3D}~Models via Learned Objective Functions},
  booktitle 		= {Workshop on Vision, Modeling, and Visualization~(VMV)},
  year      		= {2007},
  month				= {November},
  address   		= {Saarbr\"ucken, Germany},
  volume = {1},
  pages			= {233-241},
  bib2html_pubtype	= {Refereed Workshop Paper},
  bib2html_rescat   = {Image Understanding},
  bib2html_groups   = {IU},
  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 09.03.2013 19:45 by goron