Search Results - 3D convolutional autoencoder

Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection

Authors: Niaz, Asim Ul Amin, Sareer Soomro, Shafiullah et al.

Source: IEEE Access. 12:104770-104784

Subject Terms: Videos, Anomaly detection, Surveillance, Feature extraction, Predictive models, Pedestrians, Optical flow, autoencoders, computer vision, intelligent surveillance systems, three-dimensional convolutional neural network (3DCNN), video-based abnormal event detection, Computer Science, Datavetenskap

File Description: electronic

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-132046
https://doi.org/10.1109/ACCESS.2024.3435144

Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection

Authors: Rana Veer Samara Sihman Bharattej R Zaid Ajznblasm E. G. Satish et al.

Source: 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). :1-5

Data‐Driven Detection of Internal Erosion Initiation in Gap‐Graded Soils: Combining Particle‐Scale CFD‐DEM Simulation With 3D Convolutional Autoencoder

Authors: Jie Qi Negin Yousefpour Guillermo A. Narsilio et al.

Source: International Journal for Numerical and Analytical Methods in Geomechanics.

Research on 3D convolutional autoencoder enhanced metro abnormal behavior detection based on multi-level attentional memory

Authors: Run Ye Kun Zhang Cheng Zhang et al.

Source: Multimedia Tools and Applications. 84:23861-23879

Deep learning-based automated 3D inspection of helical gears using voxelized CAD models and 3D convolutional autoencoders.

Authors: Selloum, Rabia Ameddah, Hacene Brioua, Mourad

Source: International Journal of Advanced Manufacturing Technology. Dec2025, Vol. 141 Issue 7/8, p3695-3715. 21p.

Efficient Hyperspectral Data Compression using 3D Convolutional Autoencoder

Authors: Afsana Afrin Md. Al Mamun

Source: 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE). :1-5

Learning Unsupervised Visual Representations using 3D Convolutional Autoencoder with Temporal Contrastive Modeling for Video Retrieval

Authors: Vidit Kumar Vikas Tripathi Bhaskar Pant

Source: International Journal of Mathematical, Engineering and Management Sciences, Vol 7, Iss 2, Pp 272-287 (2022)

Subject Terms: content-based search, Technology, contrastive learning, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, deep learning, 02 engineering and technology, video retrieval, unsupervised learning, convolutional autoencoder, future prediction, Mathematics

Access URL: https://doaj.org/article/f0ea4cbe295b43d3bac47453f7ed0db2

The Use of 3D Convolutional Autoencoder in Fault and Fracture Network Characterization

Authors: Feng Xu Zhiyong Li Bo Wen et al.

Source: Geofluids, Vol 2021 (2021)

Subject Terms: QE1-996.5, Geology, 01 natural sciences, 3. Good health, 0105 earth and related environmental sciences

File Description: text/xhtml

Access URL: https://downloads.hindawi.com/journals/geofluids/2021/6650823.pdf
https://doaj.org/article/4d1b193c6ba045a2ae2f0dac472bae54
http://downloads.hindawi.com/journals/geofluids/2021/6650823.pdf
https://downloads.hindawi.com/journals/geofluids/2021/6650823.pdf
https://www.hindawi.com/journals/geofluids/2021/6650823/

Learning Spatiotemporal Features for Movie Scene Retrieval Using a 3D Convolutional Autoencoder

Authors: Vidit Kumar Vikas Tripathi Bhaskar Pant

Source: Computational Intelligence in Analytics and Information Systems ISBN: 9781003332312

Spatial–Spectral Joint Hyperspectral Anomaly Detection Based on a Two-Branch 3D Convolutional Autoencoder and Spatial Filtering

Authors: Shuai Lv Siwei Zhao Dandan Li et al.

Source: Remote Sensing, Vol 15, Iss 10, p 2542 (2023)

Subject Terms: hyperspectral image, anomaly detection, 3D convolutional autoencoder, spatial–spectral joint information, spatial filtering, Science

File Description: electronic resource

Relation: https://www.mdpi.com/2072-4292/15/10/2542; https://doaj.org/toc/2072-4292

Access URL: https://doaj.org/article/5fe2e6f1e37d4e21a18d268e1067c8f9

Parametric 3D Convolutional Autoencoder for the Prediction of Flow Fields in a Bed Configuration of Hot Particles

Authors: Mjalled, Ali Namdar, Reza Reineking, Lucas et al.

Subject Terms: Fluid Dynamics (physics.flu-dyn), FOS: Mathematics, FOS: Physical sciences, Physics - Fluid Dynamics, Dynamical Systems (math.DS), Mathematics - Dynamical Systems

Access URL: http://arxiv.org/abs/2307.02960

Unsupervised Spatial–Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification

Authors: Shaohui Mei Jingyu Ji Yunhao Geng et al.

Source: IEEE Transactions on Geoscience and Remote Sensing. 57:6808-6820

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902234596262382
https://ieeexplore.ieee.org/document/8694830
https://doi.org/10.1109/TGRS.2019.2908756
http://ui.adsabs.harvard.edu/abs/2019ITGRS..57.6808M/abstract
https://dblp.uni-trier.de/db/journals/tgrs/tgrs57.html#MeiJGZLD19

Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a 'Diagnostic Label-Free' Approach: Application to Schizophrenia Datasets

Authors: Hiroyuki Yamaguchi Yuki Hashimoto Genichi Sugihara et al.

Source: Frontiers in Neuroscience, Vol 15 (2021)

Subject Terms: deep learning, machine learning, neuroimaging, schizophrenia, structural MRI, convolutional autoencoder, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fnins.2021.652987/full; https://doaj.org/toc/1662-453X

Access URL: https://doaj.org/article/2a0b0e58f7b44b5485d5dc05fb7df3b1

Three-dimensional convolutional autoencoder extracts features of structural brain images with a 'diagnostic label-free' approach: Application to schizophrenia datasets

Authors: Hiroyuki Yamaguchi Yuki Hashimoto Genichi Sugihara et al.

Source: Front Neurosci
Frontiers in Neuroscience, Vol 15 (2021)

Subject Terms: schizophrenia, 03 medical and health sciences, machine learning, neuroimaging, 0302 clinical medicine, deep learning, Neurosciences. Biological psychiatry. Neuropsychiatry, structural MRI, convolutional autoencoder, RC321-571, Neuroscience, 3. Good health

Access URL: https://www.biorxiv.org/content/biorxiv/early/2020/08/25/2020.08.24.213447.full.pdf
https://www.frontiersin.org/articles/10.3389/fnins.2021.652987/pdf
https://pubmed.ncbi.nlm.nih.gov/34305514
https://doaj.org/article/2a0b0e58f7b44b5485d5dc05fb7df3b1
https://www.biorxiv.org/content/10.1101/2020.08.24.213447v1
https://www.biorxiv.org/content/biorxiv/early/2020/08/25/2020.08.24.213447.full.pdf
https://pubmed.ncbi.nlm.nih.gov/34305514/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294943
https://europepmc.org/article/MED/34305514
https://www.frontiersin.org/articles/10.3389/fnins.2021.652987/full

Automated computer-aided design of cranial implants using a deep volumetric convolutional denoising autoencoder

Authors: Morais, Ana Egger, Jan Alves, Victor et al.

Contributors: Morais, Ana Egger, Jan Alves, Victor et al.

Subject Terms: 3D shape completion, Computer-aided design (CAD), Cranial implant, Deep learning, Denoising autoencoder, Medical imaging

File Description: application/pdf

Relation: 978-3-030-16186-6; 2194-5357

Availability: https://hdl.handle.net/1822/71391

An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification

Authors: Chunyu Li Rong Cai Junchuan Yu

Source: Remote Sensing, Vol 15, Iss 2, p 451 (2023)

Subject Terms: hyperspectral, unmixing, autoencoder, deep learning, few-shot, classification, Science

File Description: electronic resource

Relation: https://www.mdpi.com/2072-4292/15/2/451; https://doaj.org/toc/2072-4292

Access URL: https://doaj.org/article/447a0868f3724b1d8bff5643a1b83879

End-to-end joint spectral–spatial compression and reconstruction of hyperspectral images using a 3D convolutional autoencoder

Authors: Yanwen Chong Linwei Chen Shaoming Pan

Source: Journal of Electronic Imaging. 30

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, 01 natural sciences

Access URL: https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-30/issue-04/041403/End-to-end-joint-spectralspatial-compression-and-reconstruction-of-hyperspectral/10.1117/1.JEI.30.4.041403.full
https://doi.org/10.1117/1.jei.30.4.041403
https://ui.adsabs.harvard.edu/abs/2021JEI....30d1403C/abstract
https://dblp.uni-trier.de/db/journals/jei/jei30.html#ChongCP21

Data‐Driven Detection of Internal Erosion Initiation in Gap‐Graded Soils: Combining Particle‐Scale CFD‐DEM Simulation With 3D Convolutional Autoencoder.

Authors: Qi, Jie Yousefpour, Negin Narsilio, Guillermo A. et al.

Source: International Journal for Numerical & Analytical Methods in Geomechanics; Dec2025, Vol. 49 Issue 17, p4225-4247, 23p

Subject Terms: EROSION, DEEP learning, DISCRETE element method, EARTH dams, SOIL cohesion, SOIL erosion, MICROSCOPICAL technique

Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder

Authors: Ekaterina Kalinicheva Jérémie Sublime Maria Trocan

Source: Remote Sensing, Vol 12, Iss 11, p 1816 (2020)

Subject Terms: satellite image time series, unsupervised learning, clustering, segmentation, 3D convolutional network, autoencoder, Science

File Description: electronic resource

Relation: https://www.mdpi.com/2072-4292/12/11/1816; https://doaj.org/toc/2072-4292

Access URL: https://doaj.org/article/924ac67a95f24211919af28f7323daf5

Research on 3D convolutional autoencoder enhanced metro abnormal behavior detection based on multi-level attentional memory.

Authors: Ye, Run Zhang, Kun Zhang, Cheng et al.

Source: Multimedia Tools & Applications; Jun2025, Vol. 84 Issue 21, p23861-23879, 19p

Subject Terms: AUTOENCODERS, DEEP learning, PASSENGER traffic, SUBWAYS, PROBLEM solving, TRAFFIC monitoring, PEDESTRIANS