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Found 7 papers, 2 papers with code
Event-based Shape from Polarization with Spiking Neural Networks
Recent advances in event-based shape determination from polarization offer a transformative approach that tackles the trade-off between speed and accuracy in capturing surface geometries.
A Physics based Machine Learning Model to characterize Room Temperature Semiconductor Detectors in 3D
Our novel approach is the first to characterize a full 3D charge transport model of the RTSD.
Boost Event-Driven Tactile Learning with Location Spiking Neurons
Moreover, to demonstrate the representation effectiveness of our proposed neurons and capture the complex spatio-temporal dependencies in the event-driven tactile data, we exploit the location spiking neurons to propose two hybrid models for event-driven tactile learning.
Event-Driven Tactile Learning with Location Spiking Neurons
In this paper, to improve the representative capabilities of existing spiking neurons, we propose a novel neuron model called "location spiking neuron", which enables us to extract features of event-based data in a novel way.
A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices
The detection and tracking of objects in the scene are done on the distorted data at the host.
Removing Blocking Artifacts in Video Streams Using Event Cameras
In this paper, we propose EveRestNet, a convolutional neural network designed to remove blocking artifacts in videostreams using events from neuromorphic sensors.
Lossy Event Compression based on Image-derived Quad Trees and Poisson Disk Sampling
The histograms are then variably sampled via Poisson Disk Sampling prioritized by the QT based segmentation map.
