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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hyundo Kim Murphy-Chutorian, E. Triesch, J. |
| Copyright Year | 2006 |
| Description | Author affiliation: Complex Systems & Cognition Lab, UCSD, La Jolla,CA (Hyundo Kim) |
| Abstract | This paper presents a robotic vision system that can be taught to recognize novel objects in a semi-autonomous manner that does not require manual labeling or segmentation of any individual training images. Instead, unfamiliar objects are simply shown to the system in varying poses and scales against cluttered background and the system automatically detects, tracks, segments, and builds representations for these objects. We demonstrate the feasibility of our approach by training the system to recognize one hundred household objects, which are presented to the system for about a minute each. Our method resembles the way that biological organisms learn to recognize objects and it paves the way for a wealth of applications in robotics and other fields. |
| Starting Page | 145 |
| Ending Page | 145 |
| File Size | 741775 |
| Page Count | 1 |
| File Format | |
| ISBN | 0769526462 |
| DOI | 10.1109/CVPRW.2006.193 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-06-17 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Computer vision Image recognition Machine vision Robot vision systems Humans Training data Biological systems Labeling Robotics and automation |
| Content Type | Text |
| Resource Type | Article |
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