Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model

MPG-Autoren
/persons/resource/persons83919

Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Franz, M., & Chahl, J. (2003). Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15 (pp. 1319-1326). Cambridge, MA, USA: MIT Press.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-DB45-0
Zusammenfassung
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for
the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show
that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.