Neural mapping and parallel optical flow computation for autonomous navigation

Heinrich H. Buelthoff; James J. Little; Hanspeter A. Mallot

doi:10.1016/0893-6080(88)90504-7

Neural mapping and parallel optical flow computation for autonomous navigation

Heinrich H. Buelthoff, James J. Little, Hanspeter A. Mallot

Department of Brain and Cognitive Engineering

Research output: Contribution to journal › Conference article › peer-review

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
Number of pages	1
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90504-7
Publication status	Published - 1988
Event	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duration: 1988 Sep 6 → 1988 Sep 10

ASJC Scopus subject areas

Cognitive Neuroscience
Artificial Intelligence

Access to Document

10.1016/0893-6080(88)90504-7

Fingerprint Dive into the research topics of 'Neural mapping and parallel optical flow computation for autonomous navigation'. Together they form a unique fingerprint.

Cite this

@article{2598242dbe834b9994001ebb01602556,

title = "Neural mapping and parallel optical flow computation for autonomous navigation",

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.",

author = "Buelthoff, {Heinrich H.} and Little, {James J.} and Mallot, {Hanspeter A.}",

year = "1988",

doi = "10.1016/0893-6080(88)90504-7",

language = "English",

volume = "1",

journal = "Neural Networks",

issn = "0893-6080",

publisher = "Elsevier Limited",

number = "1 SUPPL",

note = "International Neural Network Society 1988 First Annual Meeting ; Conference date: 06-09-1988 Through 10-09-1988",

}

TY - JOUR

T1 - Neural mapping and parallel optical flow computation for autonomous navigation

AU - Buelthoff, Heinrich H.

AU - Little, James J.

AU - Mallot, Hanspeter A.

PY - 1988

Y1 - 1988

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 - Conference article

AN - SCOPUS:0024174089

VL - 1

JO - Neural Networks

JF - Neural Networks

SN - 0893-6080

IS - 1 SUPPL

T2 - International Neural Network Society 1988 First Annual Meeting

Y2 - 6 September 1988 through 10 September 1988

ER -