Real-time 3-D object recognition using scale invariant feature transform and stereo vision
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
In 2009 4th International Conference on Autonomous Robots and Agents (February 2000), pp. 239-244.
@conference{hsu2009real,
title={Real-time 3-D object recognition using Scale Invariant Feature Transform and stereo vision},
author={Hsu, G.S. and Lin, C.Y. and Wu, J.S.},
booktitle={Autonomous Robots and Agents, 2009. ICARA 2009. 4th International Conference on},
pages={239–244},
organization={IEEE}
}
Scale invariant feature transform (SIFT) and stereo vision are applied together to recognize objects in real time. This work reports the performance of a GPU (graphic processing unit) based real-time feature detector in capturing the features of 3D objects when the objects undergo rotational and translational motions in cluttered backgrounds. We have compared the performance of the feature detector implemented upon GPU to that upon CPU, and shown that GPU-based solution has substantially outperformed its CPU counterpart.
April 11, 2011 by hgpu
