Technical University of Munich

@inproceedings{wimmer_estimating_2007,
 author = {M Wimmer and C Mayer and F Stulp and B Radig},
 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 = {Saarbrücken, 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.},
}