Results 251 to 260 of about 65,144 (299)

PMRVT: Parallel Attention Multilayer Perceptron Recurrent Vision Transformer for Object Detection with Event Cameras. [PDF]

Sensors (Basel)
Song Z, Wang J, Su Y, Sun Y, Duan X.
europepmc   +1 more source

Ultrafast Dynamic Defect Inspection With Computational Neuromorphic Imaging. [PDF]

Adv Sci (Weinh)
Zhu S, Yin Q, Wang C, Huang J, Lam EY.
europepmc   +1 more source

Artificial intelligence for monitoring hand hygiene compliance in healthcare settings: A scoping review. [PDF]

PLoS One
Lin X, Lv Y, Xiang Q, Cai M, Wang P.
europepmc   +1 more source

Optical linear systems framework for event sensing and computational neuromorphic imaging. [PDF]

Front Neurosci
Kruger N, Ralph NO, Cohen G, Hurley P.
europepmc   +1 more source

A Narrative Review on Internet of Things and Artificial Intelligence for Poultry Production. [PDF]

Animals (Basel)
Dhungana A, Paneru B, Dahal S, Chai L.
europepmc   +1 more source

Towards Event-Based State Estimation for Neuromorphic Event Cameras

IEEE Transactions on Automatic Control, 2022
In this work, a dynamic information extraction problem for neuromorphic event cameras is investigated from a state estimation perspective. The ego-motion pose estimation task of an event camera is formulated as a state estimation problem for a finite-state hidden Markov model subject to a special event-triggering mechanism.
Xinhui Liu, Meiqi Cheng, Dawei Shi, Ling Shi 0001   +3 more
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Motion Segmentation and Egomotion Estimation with Event-Based Cameras. [PDF]

, 2020
Computer vision has been dominated by classical, CMOS frame-based imaging sensors for many years. Yet, motion is not well represented in classical cameras and vision techniques - a consequence of traditional vision being frame-based and only existing 'in the moment' while motion is a continuous entity.
Mitrokhin, Anton
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Representing motion information from event-based cameras

2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2017
Many recent works have successfully leveraged motion information (i.e., dense optical flow) for a variety of problems. In this paper, we introduce a methodology to capture motion information using high-speed event-based cameras combined with convolutional neural networks (CNN).
Keith Sullivan, Wallace Lawson
openaire   +1 more source

Bayes classification for asynchronous event-based cameras

2015 23rd European Signal Processing Conference (EUSIPCO), 2015
Asynchronous event-based cameras use time encoding to code the pixel intensity values. A time encoding of an input pattern generates a random stream of asynchronous events. An event is defined as a pair containing a timestamp and the variation sign of the input signal since the last emitted event.
openaire   +2 more sources