Electromagnetic Imaging Boosted Visual Object Recognition Under Difficult Visual Conditions-Reference-Cited by-同舟云学术

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Electromagnetic Imaging Boosted Visual Object Recognition Under Difficult Visual Conditions

Published:2023 Issue: Volume:61 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Tan Min¹^ORCID,Jin Tao¹,Ye Danhui¹,Xu Kuiwen²^ORCID,Gu Xiaoling¹^ORCID,Yu Jun¹^ORCID

Affiliation:

1. Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

2. Engineering Research Center of Smart Microsensors and Microsystems, Ministry of Education, Hangzhou Dianzi University, Hangzhou, China

Funder

Zhejiang Provincial Natural Science Foundation of China

National Natural Science Foundation of China

Hangzhou Innovation Institute, Beihang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10130328.pdf?arnumber=10130328

Reference44 articles.

1. Dynamic Hand Gesture Classification Based on Multichannel Radar Using Multistream Fusion 1-D Convolutional Neural Network

2. Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses

3. Physics-aware generative adversarial networks for radar-based human activity recognition;rahman;IEEE Trans Aerosp Electron Syst,2022

4. MR Image Super-Resolution with Squeeze and Excitation Reasoning Attention Network

5. Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

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