by R Hanek, T Schmitt, S Buck and M Beetz
Abstract:
In many robot applications, autonomous robots must be capable of localizing the objects they are to manipulate. In this paper we address the object localization problem by fitting a parametric curve model to the object contour in the image. The initial prior of the object pose is iteratively refined to the posterior distribution by optimizing the separation of the object and the background. The local separation criteria are based on local statistics which are iteratively computed from the object and the background region. No prior knowledge on color distributions is needed. Experiments show that the method is capable of localizing objects in a cluttered and textured scene even under strong variations of illumination. The method is able to localize a soccer ball within frame rate.
Reference:
Fast Image-based Object Localization in Natural Scenes (R Hanek, T Schmitt, S Buck and M Beetz), In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2002, 2002.
Bibtex Entry:
@inproceedings{hanek_fast_2002,
author = {R Hanek and T Schmitt and S Buck and M Beetz},
title = {Fast Image-based Object Localization in Natural Scenes},
booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and Systems
({IROS)} 2002},
year = {2002},
series = {Lausanne},
pages = {116–122},
abstract = {In many robot applications, autonomous robots must be capable of localizing
the objects they are to manipulate. In this paper we address the
object localization problem by fitting a parametric curve model to
the object contour in the image. The initial prior of the object
pose is iteratively refined to the posterior distribution by optimizing
the separation of the object and the background. The local separation
criteria are based on local statistics which are iteratively computed
from the object and the background region. No prior knowledge on
color distributions is needed. Experiments show that the method is
capable of localizing objects in a cluttered and textured scene even
under strong variations of illumination. The method is able to localize
a soccer ball within frame rate.},
}