A deep learned nanowire segmentation model using synthetic data augmentation
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
In: npj Computational Materials, 8, Springer, https://doi.org/10.26083/tuprints-00021425
- Ereignis
-
Veröffentlichung
- (wo)
-
Darmstadt
- (wer)
-
Universitäts- und Landesbibliothek
- (wann)
-
2022
- Urheber
- DOI
-
10.26083/tuprints-00021425
- URN
-
urn:nbn:de:tuda-tuprints-214250
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:47 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Lin, Binbin
- Emami, Nima
- Santos, David A.
- Luo, Yuting
- Banerjee, Sarbajit
- Xu, Bai-Xiang
- Universitäts- und Landesbibliothek
Entstanden
- 2022
Ähnliche Objekte (12)
Smart(sampling)augment: optimal and efficient data augmentation for semantic segmentation
Segmentation-Based Building Analysis from Polarimetric Synthetic Aperture Radar Images
Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks
Acquisition, Modeling, and Augmentation of Reflectance for Synthetic Optical Flow Reference Data
Nitride nanowire heterostructures
Nitride Nanowire Heterostructures
Core-Shell Capsule Image Segmentation through Deep Learning with Synthetic Training Data
Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation
Data augmentation for semantic segmentation in the context of carbon fiber defect detection using adversarial learning
Segmentation in Tomography Data : Exploring Data Augmentation for Supervised and Unsupervised Voxel Classification with Neural Networks
Penile augmentation
Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set
Smart(sampling)augment: optimal and efficient data augmentation for semantic segmentation
Segmentation-Based Building Analysis from Polarimetric Synthetic Aperture Radar Images
Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks
Acquisition, Modeling, and Augmentation of Reflectance for Synthetic Optical Flow Reference Data
Nitride nanowire heterostructures
Nitride Nanowire Heterostructures
Core-Shell Capsule Image Segmentation through Deep Learning with Synthetic Training Data
Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation
Data augmentation for semantic segmentation in the context of carbon fiber defect detection using adversarial learning
Segmentation in Tomography Data : Exploring Data Augmentation for Supervised and Unsupervised Voxel Classification with Neural Networks
Penile augmentation
Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set
Smart(sampling)augment: optimal and efficient data augmentation for semantic segmentation
Segmentation-Based Building Analysis from Polarimetric Synthetic Aperture Radar Images
Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks
Acquisition, Modeling, and Augmentation of Reflectance for Synthetic Optical Flow Reference Data
Nitride nanowire heterostructures
Nitride Nanowire Heterostructures
Core-Shell Capsule Image Segmentation through Deep Learning with Synthetic Training Data
Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation
Data augmentation for semantic segmentation in the context of carbon fiber defect detection using adversarial learning
Segmentation in Tomography Data : Exploring Data Augmentation for Supervised and Unsupervised Voxel Classification with Neural Networks
Penile augmentation