ML Anthology

Semi-Autonomous Learning of Objects

Hyundo Kim, Erik Murphy-Chutorian, Jochen Triesch
CVPRW 2006 pp. 145
doi:10.1109/CVPRW.2006.193 /cvprw/2006/kim2006cvprw-semiautonomous/

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.

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Cite

Text

Kim et al. "Semi-Autonomous Learning of Objects." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.193

Markdown

[Kim et al. "Semi-Autonomous Learning of Objects." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/kim2006cvprw-semiautonomous/) doi:10.1109/CVPRW.2006.193

BibTeX

@inproceedings{kim2006cvprw-semiautonomous,
  title     = {{Semi-Autonomous Learning of Objects}},
  author    = {Kim, Hyundo and Murphy-Chutorian, Erik and Triesch, Jochen},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2006},
  pages     = {145},
  doi       = {10.1109/CVPRW.2006.193},
  url       = {https://mlanthology.org/cvprw/2006/kim2006cvprw-semiautonomous/}
}