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Image Understanding and Knowledge-Based Systems
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Informatik IX

Image Understanding and Knowledge-Based Systems

Boltzmannstrasse 3
85748 Garching

info@iuks.in.tum.de




Estimating Natural Activity by Fitting 3D Models via Learned Objective Functions (bibtex)
Estimating Natural Activity by Fitting 3D Models via Learned Objective Functions (bibtex)
by M Wimmer, C Mayer, F Stulp and B Radig
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.
Reference:
Estimating Natural Activity by Fitting 3D Models via Learned Objective Functions (M Wimmer, C Mayer, F Stulp and B Radig), In Workshop on Vision, Modeling, and Visualization (VMV), volume 1, 2007. 
Bibtex Entry:
@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.},
}
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Informatik IX

Image Understanding and Knowledge-Based Systems

Boltzmannstrasse 3
85748 Garching

info@iuks.in.tum.de