Results 51 to 60 of about 1,313 (183)

Classification of Holograms with 3D-CNN

Sensors, 2022
A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in ...
Dániel Terbe, László Orzó, Ákos Zarándy   +2 more
doaj   +1 more source

Unsupervised 3D Reconstruction with Multi-Measure and High-Resolution Loss

Sensors, 2022
Multi-view 3D reconstruction technology based on deep learning is developing rapidly. Unsupervised learning has become a research hotspot because it does not need ground truth labels.
Yijie Zheng, Jianxin Luo, Weiwei Chen, Yanyan Zhang, Haixun Sun, Zhisong Pan   +5 more
doaj   +1 more source

SCESNet: A Precise Semantic Segmentation Algorithm for Laser Point Clouds of Key Substation Equipment

IET Generation, Transmission &Distribution, Volume 20, Issue 1, January/December 2026.
Facing the challenge of inaccurate localization of key substation equipment in LiDAR data, this paper proposes an enhanced PointNet++ model for point cloud segmentation. By incorporating an inverted residual module, TriAtten‐transformer and the RAdam optimizer, our SCESNet achieves superior segmentation accuracy on a dedicated substation dataset ...
Gao Shuguo, Tian Xu, Liu Haoyu, Hu Chenlong, Guo Meng, Zhang Zhigang   +5 more
wiley   +1 more source

Rethinking 3D-CNN in Hyperspectral Image Super-Resolution

Remote Sensing, 2023
Recently, CNN-based methods for hyperspectral image super-resolution (HSISR) have achieved outstanding performance. Due to the multi-band property of hyperspectral images, 3D convolutions are natural candidates for extracting spatial–spectral ...
Ziqian Liu, Wenbing Wang, Qing Ma, Xianming Liu, Junjun Jiang   +4 more
doaj   +1 more source

Enhancing Rail Transit Safety: Edge Computing‐Based Driver State Monitoring System

IET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.
This article presents an edge computing‐based driver state monitoring system designed to enhance rail transit safety by analyzing real‐time data on driver identity, posture, and behavior. Leveraging facial recognition and AI‐driven detection methods, the system integrates edge and cloud computing to promptly identify unsafe conditions, enabling ...
Yiqing Liu, Gaoyun Cheng, Binbin Yuan, Hongyu Sun, Edward Stewart, Zhe Fu, Zongru Ma, Ning Zhao   +7 more
wiley   +1 more source

Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition

Computational Intelligence and Neuroscience, 2022
Three-dimensional convolutional network (3DCNN) is an essential field of motion recognition research. The research work of this paper optimizes the traditional three-dimensional convolution network, introduces the self-attention mechanism, and proposes a new network model to analyze and process complex human motion videos.
Maochang Zhu, Sheng Bin, Gengxin Sun
openaire   +2 more sources

A Semantic Segmentation Framework for Hyperspectral Imagery Based on Tucker Decomposition and 3DCNN Tested with Simulated Noisy Scenarios

, 2023
The present work, unlike others, does not try to reduce the noise in hyperspectral images to increase the semantic segmentation performance metrics; rather, we present a classification framework for noisy Hyperspectral Images (HSI), studying the ...
Andres Mendez-Vazquez, Efrain Padilla-Zepeda, Deni Torres-Roman   +2 more
core   +1 more source

Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network

Diagnostics, 2020
The early detection and rapid quantification of acute ischemic lesions play pivotal roles in stroke management. We developed a deep learning algorithm for the automatic binary classification of the Alberta Stroke Program Early Computed Tomographic Score (
Luu-Ngoc Do, Byung Hyun Baek, Seul Kee Kim, Hyung-Jeong Yang, Ilwoo Park, Woong Yoon   +5 more
doaj   +1 more source

GSDF‐Gait: A GCN and Self‐Attention Dynamic Fusion Network for Gait‐Based Person Recognition

Applied Computational Intelligence and Soft Computing, Volume 2026, Issue 1, 2026.
Graph convolutional networks (GCNs) are extensively used for skeleton‐based gait recognition. Nevertheless, despite significant improvements, a substantial challenge lies in the restricted receptive field of GCNs. However, separate structural joints could also reveal a notably important correlation.
Md. Khaliluzzaman, Kaushik Deb, Nasimul Noman   +2 more
wiley   +1 more source

Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views [PDF]

Applied Sciences, 2020
Deep learning-based methods have proven to be the best performers when it comes to object recognition cues both in images and tridimensional data. Nonetheless, when it comes to 3D object recognition, the authors tend to convert the 3D data to images and then perform their classification. However, despite its accuracy, this approach has some issues.
Francisco Gomez-Donoso, Felix Escalona, Miguel Cazorla   +2 more
openaire   +3 more sources