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.},
}