ArticleFrontiers in neuroscience2015
Spatiotemporal features for asynchronous event-based data.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Tunable Dynamics via Dual-Ion Modulation for Event-based Data Processing Using a Highly Uniform and Self-Rectifying Memristor Array.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: A Review.Sensors (Basel, Switzerland) · 2025Review
- A recurrent YOLOv8-based framework for event-based object detection.Frontiers in neuroscience · 2024Article
- Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model.Frontiers in neuroscience · 2023Article
- Real-Time Event-Based Unsupervised Feature Consolidation and Tracking for Space Situational Awareness.Frontiers in neuroscience · 2022Article
- Optogenetic therapy: high spatiotemporal resolution and pattern discrimination compatible with vision restoration in non-human primates.Communications biology · 2021Article
- Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review.Frontiers in neural circuits · 2021Review
- Event-Based Feature Extraction Using Adaptive Selection Thresholds.Sensors (Basel, Switzerland) · 2020Article
- Event-Based Gesture Recognition With Dynamic Background Suppression Using Smartphone Computational Capabilities.Frontiers in neuroscience · 2020Article
- Spiking neural networks for computer vision.Interface focus · 2018Article
- Review
- Feature Representations for Neuromorphic Audio Spike Streams.Frontiers in neuroscience · 2018Article
- Investigation of Event-Based Surfaces for High-Speed Detection, Unsupervised Feature Extraction, and Object Recognition.Frontiers in neuroscience · 2018Article
- Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.Frontiers in neurorobotics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the reliable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.
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