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Volume: 32 | Article ID: art00006
Crystallographic Symmetry for Data Augmentation in Detecting Dendrite Cores
  DOI :  10.2352/ISSN.2470-1173.2020.14.COIMG-248  Published OnlineJanuary 2020

Accurately and rapidly detecting the locations of the cores of large-scale dendrites from 2D sectioned microscopic images helps quantify the microstructure of material components. This provides a critical link between the processing and properties of the material. Such a tool could be a critical part of a quality control procedure for manufacturing these components. In this paper, we propose to use Faster R-CNN, a convolutional neural network (CNN) model that considers both the detection accuracy and computational efficiency, to detect the dendrite cores with complex shapes. However, training CNN models usually requires a large number of images annotated with ground-truth locations of dendrite cores, which are usually obtained by highly laborintensive manual annotations. In this paper, we leverage the crystallographic symmetry of dendrite cores for data augmentation – the cross sections of dendrite cores show, not perfect, but near four-fold rotation symmetry and we can rotate the image around the center of dendrite cores by specified angles to construct new training data without additional manual annotations. We conduct a series of experiments and the results show the effectiveness of the Faster R-CNN method with the proposed data augmentation strategy. Particularly, we find that we can reduce the number of the manually annotated training images by 75% while still maintaining the same detection accuracy of dendrite cores.

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Lan Fu, Hongkai Yu, Megna Shah, Jeff Simmons, Song Wang, "Crystallographic Symmetry for Data Augmentation in Detecting Dendrite Coresin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVIII,  2020,  pp 248-1 - 248-7,

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