Research graph
References from HAC-NET: A soft displacement estimation network for digital image correlation. Local targets link to admitted publications; unresolved targets remain external evidence.
Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review
10.1088/0957-0233/20/6/062001 · 2009 · External reference
A speckling technique for DIC on ultra-soft, highly hydrated materials
10.1007/s11340-023-00938-x · 2023 · External reference
Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals
10.1088/1361-6501/aac55b · 2018 · External reference
Speckle pattern quality assessment for digital image correlation
10.1016/j.optlaseng.2013.03.014 · 2013 · External reference
Digital image correlation for structural measurements
2012 · External reference
A review of speckle pattern fabrication and assessment for digital image correlation
10.1007/s11340-017-0283-1 · 2017 · External reference
The state of the art of two-dimensional digital image correlation computational method
2019 · External reference
Image feature guided self-adaptive subset configuration for digital image correlation
2025 · External reference
Directional DIC method with automatic feature selection
10.1016/j.ymssp.2024.112080 · 2025 · External reference
When deep learning meets digital image correlation
10.1016/j.optlaseng.2020.106308 · 2021 · External reference
Unsupervised CNN-based DIC method for 2D displacement measurement
10.1016/j.optlaseng.2023.107981 · 2024 · External reference
Deep DIC: deep learning-based digital image correlation for end-to-end displacement and strain measurement
10.1016/j.jmatprotec.2021.117474 · 2022 · External reference
StrainNet-LD: large displacement digital image correlation based on deep learning and displacement-field decomposition
10.1016/j.optlaseng.2024.108502 · 2024 · External reference
Efficient and robust deformation measurement based on unsupervised learning
10.1016/j.measurement.2024.115908 · 2025 · External reference
Stereo-DICNet: an efficient and unified speckle matching network for stereo digital image correlation measurement
10.1016/j.optlaseng.2024.108267 · 2024 · External reference
Deep 3D-DIC using a coarse-to-fine network for robust and accurate 3D shape and displacement measurements
10.1364/oe.549759 · 2025 · External reference
Transformer based deep learning for digital image correlation
10.1016/j.optlaseng.2024.108568 · 2025 · External reference
U-Net: convolutional networks for biomedical image segmentation
2015 · External reference
FlowNet: learning optical flow with convolutional networks
2015 · External reference
FlowNet 2.0: evolution of optical flow estimation with deep networks
2017 · External reference
DIC-net: upgrade the performance of traditional DIC with hermite dataset and convolution neural network
10.1016/j.optlaseng.2022.107278 · 2023 · External reference
Feature pyramid networks for object detection
2017 · External reference
RAFT: recurrent all-pairs field transforms for optical flow
2020 · External reference
A gated recurrent units (GRU)-based model for early detection of soybean sudden death syndrome through time-series satellite imagery
10.3390/rs12213621 · 2020 · External reference
Learning to estimate hidden motions with global motion aggregation
2021 · External reference
Attention is all you need
2017 · External reference
An image is worth 16×16 words: transformers for image recognition at scale
2021 · External reference
Swin transformer: hierarchical vision transformer using shifted windows
2021 · External reference
Swin transformer v2: scaling up capacity and resolution
2022 · External reference
FlowFormer: a transformer architecture for optical flow
2022 · External reference
GMFlow: learning optical flow via global matching
2022 · External reference
Deep residual learning for image recognition
2016 · External reference
Sub-pixel displacement measurement with swin transformer: a three-level classification approach
10.3390/app15052868 · 2025 · External reference
Transformer-enhanced end-to-end models for accurate displacement and strain fields in digital image correlation
10.1364/oe.553602 · 2025 · External reference
Hyperspectral image classification based on ConvGRU and spectral–spatial joint attention
10.1016/j.asoc.2025.112949 · 2025 · External reference
Convolutional LSTM network: a machine learning approach for precipitation nowcasting
2015 · External reference
SigMa: semantic similarity-guided semi-dense feature matching
10.1109/tip.2026.3654367 · 2026 · External reference
SuperGlue: learning feature matching with graph neural networks
2020 · External reference
Adam: a method for stochastic optimization
2015 · External reference
Super-convergence: very fast training of neural networks using large learning rates
2019 · External reference
SGDR: stochastic gradient descent with warm restarts
2017 · External reference
μDIC: an open-source toolkit for digital image correlation
10.1016/j.softx.2019.100391 · 2020 · External reference
Ncorr: open-source 2D digital image correlation MATLAB software
10.1007/s11340-015-0009-1 · 2015 · External reference
Ncorr: open-source 2D digital image correlation MATLAB software
10.1007/s11340-015-0009-1 · ExternalCitation · doi-reference
A review of speckle pattern fabrication and assessment for digital image correlation
10.1007/s11340-017-0283-1 · ExternalCitation · doi-reference
A speckling technique for DIC on ultra-soft, highly hydrated materials
10.1007/s11340-023-00938-x · ExternalCitation · doi-reference
Hyperspectral image classification based on ConvGRU and spectral–spatial joint attention
10.1016/j.asoc.2025.112949 · ExternalCitation · doi-reference
Deep DIC: deep learning-based digital image correlation for end-to-end displacement and strain measurement
10.1016/j.jmatprotec.2021.117474 · ExternalCitation · doi-reference
Efficient and robust deformation measurement based on unsupervised learning
10.1016/j.measurement.2024.115908 · ExternalCitation · doi-reference
Speckle pattern quality assessment for digital image correlation
10.1016/j.optlaseng.2013.03.014 · ExternalCitation · doi-reference
When deep learning meets digital image correlation
10.1016/j.optlaseng.2020.106308 · ExternalCitation · doi-reference
DIC-net: upgrade the performance of traditional DIC with hermite dataset and convolution neural network
10.1016/j.optlaseng.2022.107278 · ExternalCitation · doi-reference
Unsupervised CNN-based DIC method for 2D displacement measurement
10.1016/j.optlaseng.2023.107981 · ExternalCitation · doi-reference
Stereo-DICNet: an efficient and unified speckle matching network for stereo digital image correlation measurement
10.1016/j.optlaseng.2024.108267 · ExternalCitation · doi-reference
StrainNet-LD: large displacement digital image correlation based on deep learning and displacement-field decomposition
10.1016/j.optlaseng.2024.108502 · ExternalCitation · doi-reference
Transformer based deep learning for digital image correlation
10.1016/j.optlaseng.2024.108568 · ExternalCitation · doi-reference
μDIC: an open-source toolkit for digital image correlation
10.1016/j.softx.2019.100391 · ExternalCitation · doi-reference
Directional DIC method with automatic feature selection
10.1016/j.ymssp.2024.112080 · ExternalCitation · doi-reference
Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review
10.1088/0957-0233/20/6/062001 · ExternalCitation · doi-reference
Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals
10.1088/1361-6501/aac55b · ExternalCitation · doi-reference
SigMa: semantic similarity-guided semi-dense feature matching
10.1109/tip.2026.3654367 · ExternalCitation · doi-reference
Deep 3D-DIC using a coarse-to-fine network for robust and accurate 3D shape and displacement measurements
10.1364/oe.549759 · ExternalCitation · doi-reference
Transformer-enhanced end-to-end models for accurate displacement and strain fields in digital image correlation
10.1364/oe.553602 · ExternalCitation · doi-reference
Sub-pixel displacement measurement with swin transformer: a three-level classification approach
10.3390/app15052868 · ExternalCitation · doi-reference
A gated recurrent units (GRU)-based model for early detection of soybean sudden death syndrome through time-series satellite imagery
10.3390/rs12213621 · ExternalCitation · doi-reference
