Vergence Using GPU Cepstral Filtering
Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Columbia – Polo II, 3030-290 Coimbra, Portugal
Technological Innovation for Sustainability, IFIP Advances in Information and Communication Technology, Volume 349/2011, 325-332, 2011
@article{almeida2011vergence,
title={Vergence Using GPU Cepstral Filtering},
author={Almeida, L. and Menezes, P. and Dias, J.},
journal={Technological Innovation for Sustainability},
pages={325–332},
year={2011},
publisher={Springer}
}
Vergence ability is an important visual behavior observed on living creatures when they use vision to interact with the environment. The notion of active observer is equally useful for robotic vision systems on tasks like object tracking, fixation and 3D environment structure recovery. Humanoid robotics are a potential playground for such behaviors. This paper describes the implementation of a real time binocular vergence behavior using cepstral filtering to estimate stereo disparities. By implementing the cepstral filter on a graphics processing unit (GPU) using Compute Unified Device Architecture (CUDA) we demonstrate that robust parallel algorithms that used to require dedicated hardware are now available on common computers. The overall system is implemented in the binocular vision system IMPEP (IMPEP Integrated Multimodal Perception Experimental Platform) to illustrate the system performance experimentally.
November 7, 2011 by hgpu
