By Topic

An efficient neuromorphic analog network for motion estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
A. B. Torralba ; Inst. Nat. Polytech. de Grenoble, France ; J. Herault

Optical flow estimation is a critical mechanism for autonomous mobile robots as it provides a range of useful information. As real-time processing is mandatory in this case, an efficient solution is the use of specific very large scale integration (VLSI) analog circuits. This paper presents a simple and regular architecture based on analog circuits, which implements the entire processing line from photoreceptor to accurate and reliable optical flow estimation. The algorithm we propose, is an energy-based method using a novel wideband velocity-tuned filter which proves to be an efficient alternative to the well-known Gabor filters. Our approach shows that a high level of accuracy can be obtained from a small number of loosely tuned filters. It exhibits similar or improved performance to that of other existing algorithms, but with a much lower complexity

Published in:

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications  (Volume:46 ,  Issue: 2 )