Zobrazeno 1 - 10
of 161
pro vyhledávání: '"Kenneth Chaney"'
Publikováno v:
Dev Dyn
Background Differences in core or tissue-specific ribosomal protein (Rp) composition within ribosomes contribute to ribosome heterogeneity and functional variability. Yet, the degree to which ribosome heterogeneity modulates development is unknown. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6d29643f10d0fadf5dca877f06d200d
https://doi.org/10.1002/dvdy.185
https://doi.org/10.1002/dvdy.185
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200700
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6031327b6c79c26fc12c7e4edfb3df12
https://doi.org/10.1007/978-3-031-20071-7_17
https://doi.org/10.1007/978-3-031-20071-7_17
Autor:
Karl Schmeckpeper, Philip Osteen, Yufu Wang, Georgios Pavlakos, Kenneth Chaney, Wyatt Jordan, Xiaowei Zhou, Konstantinos Derpanis, Kostas Daniilidis
This paper presents an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. Unlike prior investig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7da34f1844d83adea56a71ddd835a885
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c763dadb5e26d2c89dbb46e7b3ef627
https://doi.org/10.1109/iros51168.2021.9635975
https://doi.org/10.1109/iros51168.2021.9635975
Publikováno v:
IJCNN
Spiking neural networks (SNNs) are a promising candidate for biologically-inspired and energy efficient computation. However, their simulation is restrictively time consuming, and creates a bottleneck in developing competitive training methods with p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b7fcec82a51d6c9699a2c44bf736b91
https://doi.org/10.1109/ijcnn48605.2020.9207452
https://doi.org/10.1109/ijcnn48605.2020.9207452
Autor:
Kaushik Roy, Alex Zihao Zhu, Kostas Daniilidis, Chankyu Lee, Adarsh Kumar Kosta, Kenneth Chaney
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030585259
ECCV (29)
ECCV (29)
Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based cameras suffer critically. This is attributed to their high temporal resolu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25ff29d42f5903a556227fbc8c2114e6
https://doi.org/10.1007/978-3-030-58526-6_22
https://doi.org/10.1007/978-3-030-58526-6_22
Publikováno v:
CVPR Workshops
IROS
IROS
Event-based cameras are a novel asynchronous sensing modality that provide exciting benefits, such as the ability to track fast moving objects with no motion blur and low latency, high dynamic range, and low power consumption. Given the low latency o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::348dbd9473d05462f22bf5a1dc9ab61e
https://doi.org/10.1109/cvprw.2019.00206
https://doi.org/10.1109/cvprw.2019.00206
Publikováno v:
CVPR Workshops
We propose a demo of our work, Unsupervised Event-based Learning of Optical Flow, Depth and Egomotion, which will also appear at CVPR 2019. Our demo consists of a CNN which takes as input events from a DAVIS-346b event camera, represented as a discre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3eda47d64f056247721857ae1ec74543
https://doi.org/10.1109/cvprw.2019.00216
https://doi.org/10.1109/cvprw.2019.00216
Autor:
Benjamin Caimano, Kenneth Chaney, Todd W. Danko, David Kotfis, Steven Gray, Kingsley Fregene, Aron Rubin, Robert Chevalier
Publikováno v:
Journal of Field Robotics. 34:852-873
Recent robotics efforts have automated simple, repetitive tasks to increase execution speed and lessen an operator’s cognitive load, allowing them to focus on higher-level objectives. However, an autonomous system will eventually encounter somethin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4b81b3d69d3ac43b299ac17c53941e6
https://doi.org/10.1002/rob.21684
https://doi.org/10.1002/rob.21684
Publikováno v:
CVPR
In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input representation of the events in the form of a discretized volume that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16c2f934e4cdf5c0a08aa0cf655264f0
http://arxiv.org/abs/1812.08156
http://arxiv.org/abs/1812.08156