Automatized object identification and feature analysis of experimental image data are indispensab... more Automatized object identification and feature analysis of experimental image data are indispensable for data-driven material science; deep learning-based segmentation algorithms have been shown to be a promising technique to achieve this goal. However, acquiring of high-resolution experimental images and assigning labels in order to train such algorithms is challenging and costly in terms of both time and labor expense. In the present work, we apply synthetic images, which resemble the experimental image data in terms of geometrical and visual features, to train state-of-art deep learning-based Mask R-CNN algorithms to segment vanadium pentoxide (V2O5) nanowires, a canonical cathode material within optical intensity-based images from spectromicroscopy. The performance evaluation demonstrates that even though the deep learning model is trained on pure synthetically generated structures, it can segment real optical intensity-based spectromicroscopy images of complex V2O5 nanowire stru...
Automatized object identification and feature analysis of experimental image data are indispensab... more Automatized object identification and feature analysis of experimental image data are indispensable for data-driven material science; deep learning-based segmentation algorithms have been shown to be a promising technique to achieve this goal. However, acquiring of high-resolution experimental images and assigning labels in order to train such algorithms is challenging and costly in terms of both time and labor expense. In the present work, we apply synthetic images, which resemble the experimental image data in terms of geometrical and visual features, to train state-of-art deep learning-based Mask R-CNN algorithms to segment vanadium pentoxide (V2O5) nanowires, a canonical cathode material within optical intensity-based images from spectromicroscopy. The performance evaluation demonstrates that even though the deep learning model is trained on pure synthetically generated structures, it can segment real optical intensity-based spectromicroscopy images of complex V2O5 nanowire stru...
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Papers by Nima Emami