Abstract
We present two information processing strategies, derived from neurobiology, which facilitate the evaluation of optical flow data considerably. One is a parallel motion algorithm, and the other is an inverse perspective mapping technique. The combination of the two implements the following principles of biological information processing: (1) Spatially dense motion data are obtained which are not biased by the aperture problem. (2) The computational resources available can be utilized most effectively by transforming space-variant problems into space-invariant ones. (3) The definition of an obstacle is reduced to its most basic meaning: it is only the elevation above the ground plane that leads to a detection and pattern recognition is not necessary at this stage. This integrated approach has been successfully tested on real-time robot navigation applications.
| Original language | English |
|---|---|
| Journal | Neural Networks |
| Volume | 1 |
| Issue number | 1 SUPPL |
| DOIs | |
| Publication status | Published - 1988 Dec 1 |
| Externally published | Yes |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
- Neuroscience(all)
Cite this
Neural mapping and parallel optical flow computation for autonomous navigation. / Bulthoff, Heinrich; Little, James J.; Mallot, Hanspeter A.
In: Neural Networks, Vol. 1, No. 1 SUPPL, 01.12.1988.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Neural mapping and parallel optical flow computation for autonomous navigation
AU - Bulthoff, Heinrich
AU - Little, James J.
AU - Mallot, Hanspeter A.
PY - 1988/12/1
Y1 - 1988/12/1
N2 - We present two information processing strategies, derived from neurobiology, which facilitate the evaluation of optical flow data considerably. One is a parallel motion algorithm, and the other is an inverse perspective mapping technique. The combination of the two implements the following principles of biological information processing: (1) Spatially dense motion data are obtained which are not biased by the aperture problem. (2) The computational resources available can be utilized most effectively by transforming space-variant problems into space-invariant ones. (3) The definition of an obstacle is reduced to its most basic meaning: it is only the elevation above the ground plane that leads to a detection and pattern recognition is not necessary at this stage. This integrated approach has been successfully tested on real-time robot navigation applications.
AB - We present two information processing strategies, derived from neurobiology, which facilitate the evaluation of optical flow data considerably. One is a parallel motion algorithm, and the other is an inverse perspective mapping technique. The combination of the two implements the following principles of biological information processing: (1) Spatially dense motion data are obtained which are not biased by the aperture problem. (2) The computational resources available can be utilized most effectively by transforming space-variant problems into space-invariant ones. (3) The definition of an obstacle is reduced to its most basic meaning: it is only the elevation above the ground plane that leads to a detection and pattern recognition is not necessary at this stage. This integrated approach has been successfully tested on real-time robot navigation applications.
UR - http://www.scopus.com/inward/record.url?scp=0024174089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0024174089&partnerID=8YFLogxK
U2 - 10.1016/0893-6080(88)90504-7
DO - 10.1016/0893-6080(88)90504-7
M3 - Article
AN - SCOPUS:0024174089
VL - 1
JO - Neural Networks
JF - Neural Networks
SN - 0893-6080
IS - 1 SUPPL
ER -