TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Ganter, Joshua
A1  - Löffler, Simon
A1  - Metzger, Ron
A1  - Ußling, Katharina
A1  - Müller, Christoph
T1  - Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks
JF  - Journal of Imaging
N2  - Collecting real-world data for the training of neural networks is enormously time-consuming and expensive. As such, the concept of virtualizing the domain and creating synthetic data has been analyzed in many instances. This virtualization offers many possibilities of changing the domain, and with that, enabling the relatively fast creation of data. It also offers the chance to enhance necessary augmentations with additional semantic information when compared with conventional augmentation methods. This raises the question of whether such semantic changes, which can be seen as augmentations of the virtual domain, contribute to better results for neural networks, when trained with data augmented this way. In this paper, a virtual dataset is presented, including semantic augmentations and automatically generated annotations, as well as a comparison between semantic and conventional augmentation for image data. It is determined that the results differ only marginally for neural network models trained with the two augmentation approaches.
KW  - Semantic augmentation
KW  - Image segmentation
KW  - Convolutional neural networks
KW  - Virtual image data
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-77890
SN  - 2313-433X
SS  - 2313-433X
U6  - https://doi.org/https://doi.org/10.3390/jimaging7080146
DO  - https://doi.org/https://doi.org/10.3390/jimaging7080146
VL  - 07.2021
IS  - 8
SP  - 146
EP  - 162
ER  -