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- W2947526197 abstract "Any visual sensor, whether artificial or biological, maps the 3D-world on a 2D-representation. The missing dimension is depth and most species use stereo vision to recover it. Stereo vision implies multiple perspectives and matching, hence it obtains depth from a pair of images. Algorithms for stereo vision are also used prosperously in robotics. Although, biological systems seem to compute disparities effortless, artificial methods suffer from high energy demands and latency. The crucial part is the correspondence problem; finding the matching points of two images. The development of event-based cameras, inspired by the retina, enables the exploitation of an additional physical constraint-time. Due to their asynchronous course of operation, considering the precise occurrence of spikes, Spiking Neural Networks take advantage of this constraint. In this work, we investigate sensors and algorithms for event-based stereo vision leading to more biologically plausible robots. Hereby, we focus mainly on binocular stereo vision." @default.
- W2947526197 created "2019-06-07" @default.
- W2947526197 creator A5008282515 @default.
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- W2947526197 date "2019-05-28" @default.
- W2947526197 modified "2023-10-10" @default.
- W2947526197 title "Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms" @default.
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- W2947526197 doi "https://doi.org/10.3389/fnbot.2019.00028" @default.
- W2947526197 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6546825" @default.
- W2947526197 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31191287" @default.
- W2947526197 hasPublicationYear "2019" @default.
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