Cargando…
ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion Estimation
Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the e...
Autor principal: | Layton, Oliver W. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708706/ https://www.ncbi.nlm.nih.gov/pubmed/34960310 http://dx.doi.org/10.3390/s21248217 |
Ejemplares similares
-
Distributed encoding of curvilinear self-motion across spiral optic flow patterns
por: Layton, Oliver W., et al.
Publicado: (2022) -
Contrast independent biologically inspired translational optic flow estimation
por: Skelton, Phillip S. M., et al.
Publicado: (2022) -
Combining biological motion perception with optic flow analysis for self-motion in crowds
por: Hülemeier, Anna-Gesina, et al.
Publicado: (2020) -
Insect-Inspired Self-Motion Estimation with Dense Flow Fields—An Adaptive Matched Filter Approach
por: Strübbe, Simon, et al.
Publicado: (2015) -
Walking humans and running mice: perception and neural encoding of optic flow during self-motion
por: Horrocks, Edward A. B., et al.
Publicado: (2023)