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Minimally Supervised Acquisition of 3D Recognition Models from Images |
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@TechReport{Selinger:TR00b,
author = {Andrea Selinger and Randal C. Nelson},
title = {Minimally Supervised Acquisition of 3D Recognition Models from Images},
institution = {Comp. Sci. Dept University of Rochester},
year = {2000},
number = {724},
OPTaddress = {Rochester NY},
OPTmonth = {January},
abstract = {
Image-based object recognition systems developed recently don't require the construction of a
3D geometric model, allowing recognition of objects for which current geometric recognition
technologies do not apply. Such systems are typically trained with labeled, clean views that
cover the whole viewing sphere and can sometimes handle generic, visually similar classes with
moderate variation. 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 report we investigate how an object recognition system developed previously can be trained
from clean images of objects with minimal supervision. After training this system on a single or
a small number of views of each object, a simple learning algorithm is able to attract additional
views to the object representation, building clusters of views belonging to the same object. We
explore how the learning performance improves by extending the set of views, introducing a small
amount of supervision, or using more complicated learning algorithms.
}
}