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Material strain image segmentation algorithm based on impro..:

, In: 9th International Symposium on Test Automation & Instrumentation (ISTAI 2022),

Guan, Y. ; Pang, Z. ; Sun, H.. - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.3264

RT 
T1 <cTitle>9th International Symposium on Test Automation & Instrumentation (ISTAI 2022)</cTitle>: T1 <Title>Material strain image segmentation algorithm based on improved UNET network</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10026788&Exemplar=1&LAN=DE
				
A1 Guan, Y.
A1 Pang, Z.
A1 Sun, H.
A1 Chen, X.
YR 2022
K1 convolutional neural nets
K1 deep learning (artificial intelligence)
K1 deformation
K1 image segmentation
K1 interpolation
K1 mechanical engineering computing
K1 sheet materials
K1 sheet metal processing
K1 tensile strength
K1 tensile testing
K1 anisotropic materials
K1 biaxial tensile mechanical properties
K1 biaxial tensile testing machines
K1 convolution neural network
K1 deep learning image segmentation algorithm
K1 encoder-decoder network structure
K1 forming mechanism
K1 input images
K1 loading paths
K1 marked deformation region
K1 material strain image segmentation algorithm
K1 metal materials
K1 sheet metal material forming
K1 unet network
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.3264
DO https://doi.org/10.1049/icp.2022.3264
SF ELIB - SuUB Bremen

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