ArticleFrontiers in neuroscience2016
A Motion-Based Feature for Event-Based Pattern Recognition.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Event-Based Visual/Inertial Odometry for UAV Indoor Navigation.Sensors (Basel, Switzerland) · 2024Article
- Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.Biomimetics (Basel, Switzerland) · 2024Review
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review.Frontiers in neural circuits · 2021Review
- Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks.Frontiers in neuroscience · 2020Article
- Event-Based Gesture Recognition With Dynamic Background Suppression Using Smartphone Computational Capabilities.Frontiers in neuroscience · 2020Article
- A Noise Filtering Algorithm for Event-Based Asynchronous Change Detection Image Sensors on TrueNorth and Its Implementation on TrueNorth.Frontiers in neuroscience · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper introduces an event-based luminance-free feature from the output of asynchronous event-based neuromorphic retinas. The feature consists in mapping the distribution of the optical flow along the contours of the moving objects in the visual scene into a matrix. Asynchronous event-based neuromorphic retinas are composed of autonomous pixels, each of them asynchronously generating "spiking" events that encode relative changes in pixels' illumination at high temporal resolutions. The optical flow is computed at each event, and is integrated locally or globally in a speed and direction coordinate frame based grid, using speed-tuned temporal kernels. The latter ensures that the resulting feature equitably represents the distribution of the normal motion along the current moving edges, whatever their respective dynamics. The usefulness and the generality of the proposed feature are demonstrated in pattern recognition applications: local corner detection and global gesture recognition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.