3 citations · 6 across the 8 of their papers we have counts for
8 papers
Real-time 6-DoF Pose Estimation by an Event-based Camera using Active LED Markers
Gerald Ebmer, Adam Loch, Minh Nhat Vu +5
Real-time applications for autonomous operations depend largely on fast and robust vision-based localization systems. Since image processing tasks require processing large amounts…
Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems
Matteo Cartiglia, Arianna Rubino, Shyam Narayanan +4
The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip on…
Bio-inspired Polarization Event Camera
Germain Haessig, Damien Joubert, Justin Haque +4
The stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solution…
Event-Based high-speed low-latency fiducial marker tracking
Adam Loch, Germain Haessig, Markus Vincze
Motion and dynamic environments, especially under challenging lighting conditions, are still an open issue for robust robotic applications. In this paper, we propose an end-to-end…
Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks
Nik Dennler, Germain Haessig, Matteo Cartiglia +1
Vibration patterns yield valuable information about the health state of a running machine, which is commonly exploited in predictive maintenance tasks for large industrial systems.…
An error-propagation spiking neural network compatible with neuromorphic processors
Matteo Cartiglia, Germain Haessig, Giacomo Indiveri
Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing on-chip learning algori…
A Sparse Coding Multi-Scale Precise-Timing Machine Learning Algorithm for Neuromorphic Event-Based Sensors
Germain Haessig, Ryad Benosman
This paper introduces an unsupervised compact architecture that can extract features and classify the contents of dynamic scenes from the temporal output of a neuromorphic asynchro…
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
Germain Haessig, Andrew Cassidy, Rodrigo Alvarez +2
This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combine…