A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Segmentation of backscattered electron images of geopolymers using convolutional autoencoder network
Segmentation of backscattered electron (BSE) images of cementitious materials is often used to quantify different microstructural features for the sake of performance estimation at macro-scale levels. However, the heterogeneous nature of cementitious matrices compounds with varying imaging conditions can lead the conventional segmentation methods to a processing bottleneck for largescale experiments. To overcome these challenges, in this study, we evaluate the potential of deep autoencoder convolutional networks, specifically SegNet, for automatic segmentation of fly ash-based geopolymer images. We present the SegNet power in achieving a comparable accuracy to the human performance even with a few BSE images in the model’s training. The SegNet demonstrates magnification independent training that enables test image processing with both seen and unseen magnification levels. A comparative study shows that SegNet outperforms the Gaussian method on uncontrolled imaging conditions such as background brightness levels. In addition, we demonstrate the self-learning capability of SegNet in poorly annotated areas.
Segmentation of backscattered electron images of geopolymers using convolutional autoencoder network
Segmentation of backscattered electron (BSE) images of cementitious materials is often used to quantify different microstructural features for the sake of performance estimation at macro-scale levels. However, the heterogeneous nature of cementitious matrices compounds with varying imaging conditions can lead the conventional segmentation methods to a processing bottleneck for largescale experiments. To overcome these challenges, in this study, we evaluate the potential of deep autoencoder convolutional networks, specifically SegNet, for automatic segmentation of fly ash-based geopolymer images. We present the SegNet power in achieving a comparable accuracy to the human performance even with a few BSE images in the model’s training. The SegNet demonstrates magnification independent training that enables test image processing with both seen and unseen magnification levels. A comparative study shows that SegNet outperforms the Gaussian method on uncontrolled imaging conditions such as background brightness levels. In addition, we demonstrate the self-learning capability of SegNet in poorly annotated areas.
Segmentation of backscattered electron images of geopolymers using convolutional autoencoder network
Sheiati, Shohreh (author) / Behboodi, Sanaz (author) / Ranjbar, Navid (author)
2022-01-01
Sheiati , S , Behboodi , S & Ranjbar , N 2022 , ' Segmentation of backscattered electron images of geopolymers using convolutional autoencoder network ' , Expert Systems with Applications , vol. 206 , 117846 . https://doi.org/10.1016/j.eswa.2022.117846
Article (Journal)
Electronic Resource
English
DDC:
624
Pore segmentation of cement-based materials from backscattered electron images
| Online Contents | 2006
Pore segmentation of cement-based materials from backscattered electron images
| British Library Online Contents | 2006
Pore segmentation of cement-based materials from backscattered electron images
| Tema Archive | 2006
Electron Backscattered Diffraction
| British Library Online Contents | 2004
Estimating transport properties of mortars using image analysis on backscattered electron images
| Online Contents | 2006