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2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1
Minimally Supervised Acquisition of 3D Recognition Models from Cluttered Images
Kauai, Hawaii
December 08-December 14
ISBN: 0-7695-1272-0
ASCII Text
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Andrea Selinger, Randal C. Nelson, "Minimally Supervised Acquisition of 3D Recognition Models from Cluttered Images," Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 1, pp. 213, 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1, 2001.
 
 
BibTex
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@article{ 10.1109/CVPR.2001.990478,
author = {Andrea Selinger and Randal C. Nelson},
title = {Minimally Supervised Acquisition of 3D Recognition Models from Cluttered Images},
journal ={Computer Vision and Pattern Recognition, IEEE Computer Society Conference on},
volume = {1},
year = {2001},
issn = {1063-6919},
pages = {213},
doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2001.990478},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
TI - Minimally Supervised Acquisition of 3D Recognition Models from Cluttered Images
SN - 1063-6919
SP
EP
A1 - Andrea Selinger,
A1 - Randal C. Nelson,
PY - 2001
KW - null
VL - 1
JA - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
ER -
 
Andrea Selinger, University of Rochester
Randal C. Nelson, University of Rochester
Appearance-based object recognition systems rely on training from imagery, which allows the recognition of objects without requiring a 3D geometric model. It has been little explored whether such systems can be trained from imagery that is unlabeled, and whether they can be trained from imagery that is not trivially segmentable.
In this paper we present a method for minimally supervised training of a previously developed recognition system from unlabeled and unsegmented imagery. We show that the system can successfully extend an object representation extracted from one black background image to contain object features extracted from unlabeled cluttered images and can use the extended representation to improve recognition performance on a test set.
Citation:
Andrea Selinger, Randal C. Nelson, "Minimally Supervised Acquisition of 3D Recognition Models from Cluttered Images," cvpr, vol. 1, pp.213, 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1, 2001
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