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Matthias Wimmer, Christoph Mayer, Freek Stulp, and Bernd Radig. Estimating Natural Activity by Fitting 3D Models via Learned Objective Functions. In Vision, Modeling and Visualization Workshop (VMV), 2007. Non-archival.
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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.
BibTeX Entry
@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 = {Vision, Modeling and Visualization Workshop (VMV)},
note = {Non-archival.},
bib2html_pubtype = {Refereed Workshop Paper},
bib2html_rescat = {Image Understanding - Face Model Fitting},
year = {2007}
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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