By Topic

Learning 3D recognition models for general objects from unlabeled imagery: an experiment in intelligent brute force

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
R. C. Nelson ; Dept. of Comput. Sci., Rochester Univ., NY, USA ; A. Selinger

In this paper we explorer the problem of training a general, 3D abject recognition system from unlabeled imagery. In particular, we attempt to identify critical issues and stumbling blocks associated with minimizing the supervision necessary to train such a system. As class learning seems to be a relatively slow and resource intensive process even for people, we consider approaches and perform experiments that entail on the order of 1015 basic operations, even for relatively small databases. This is the current practical limit of the computation that can be achieved. For experiments, we use a recognition system developed previously

Published in:

Pattern Recognition, 2000. Proceedings. 15th International Conference on  (Volume:1 )

Date of Conference:

2000