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Found 5 papers, 3 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.
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.
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings
Macro x-ray fluorescence (XRF) imaging of cultural heritage objects, while a popular non-invasive technique for providing elemental distribution maps, is a slow acquisition process in acquiring high signal-to-noise ratio XRF volumes.
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
We propose an XRF image inpainting approach to address the issue of long scanning time, thus speeding up the scanning process while still maintaining the possibility to reconstruct a high quality XRF image.
