Results 61 to 70 of about 148,198 (270)

Dynamic gesture recognition based on 2D convolutional neural network and feature fusion

Scientific Reports, 2022
Gesture recognition is one of the most popular techniques in the field of computer vision today. In recent years, many algorithms for gesture recognition have been proposed, but most of them do not have a good balance between recognition efficiency and ...
Jimin Yu, Maowei Qin, Shangbo Zhou
doaj   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Optimal LiDAR Data Resolution Analysis for Object Classification

Sensors, 2022
When classifying objects in 3D LiDAR data, it is important to use efficient collection methods and processing algorithms. This paper considers the resolution needed to classify 3D objects accurately and discusses how this resolution is accomplished for ...
Marjorie Darrah, Matthew Richardson, Bradley DeRoos, Mitchell Wathen   +3 more
doaj   +1 more source

Electron–Matter Interactions During Electron Beam Nanopatterning

Advanced Functional Materials, EarlyView.
This article reviews the electron–matter interactions important to nanopatterning with electron beam lithography (EBL). Electron–matter interactions, including secondary electron generation routes, polymer radiolysis, and electron beam induced charging, are discussed.
Camila Faccini de Lima, Prathiba Thamizhavel, Taylor J. Woehl   +2 more
wiley   +1 more source

A Cross-Season Correspondence Dataset for Robust Semantic Segmentation

, 2019
In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmentation.
Hammarstrand, Lars, Kahl, Fredrik, Larsson, Måns, Pollefeys, Mark, Sattler, Torsten, Stenborg, Erik   +5 more
core   +1 more source

SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer

Brain Sciences, 2022
Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions.
Yun Jiang, Yuan Zhang, Xin Lin, Jinkun Dong, Tongtong Cheng, Jing Liang   +5 more
doaj   +1 more source

Intrinsic Nanopore‐Assisted SnP2S6 Memristors With Ti Ion Dynamics for Compact Logic‐In‐Memory Hardware

Advanced Functional Materials, EarlyView.
Tin hexathiophosphate memristors leverage intrinsic nanopores together with a guided filament formation strategy to regulate titanium ion motion and switching behavior. The devices support reliable nonvolatile memory and reconfigurable logic‐in‐memory, demonstrating 14 Boolean logic functions in a single cell.
Thaw Tint Te Tun, Sanchali Mitra, Hongxi Su, Jiali Huo, Haofei Zheng, Michel Bosman, Yee Sin Ang, Kah‐Wee Ang   +7 more
wiley   +1 more source

Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos

, 2017
Deep learning has been demonstrated to achieve excellent results for image classification and object detection. However, the impact of deep learning on video analysis (e.g.
Chen, Chen, Hou, Rui, Shah, Mubarak
core   +1 more source

Research on Underwater Acoustic Target Recognition Based on a 3D Fusion Feature Joint Neural Network

Journal of Marine Science and Engineering
In the context of a complex marine environment, extracting and recognizing underwater acoustic target features using ship-radiated noise present significant challenges.
Weiting Xu, Xingcheng Han, Yingliang Zhao, Liming Wang, Caiqin Jia, Siqi Feng, Junxuan Han, Li Zhang   +7 more
doaj   +1 more source

Video‐based action recognition using spurious‐3D residual attention networks

IET Image Processing, 2022
Recently, 3D Convolutional Neural Networks (3D CNNs) have attracted extensive attention in extracting spatial and temporal features in videos for their efficient feature extraction ability.
Bo Chen, Hongying Tang, Zebin Zhang, Guanjun Tong, Baoqing Li   +4 more
doaj   +1 more source