Semi-autonomous Learning of Objects

Authors:
Hyundo Kim;Erik Murphy-Chutorian;Jochen Triesch
Affiliations:
Complex Systems & Cognition Lab, UCSD, La Jolla,CA;Complex Systems & Cognition Lab, UCSD, La Jolla,CA;Frankfurt Institute for Advanced Studies, Germany
Venue:
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Year:
2006

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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.