Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is complex due to the high intra-class dissimilarity, high inter-class similarity, and multi-spatial scales of patterns. However, existing methods are ineffective in learning complex image patterns. We propose an effective end-to-end electron micrograph representation learning-based framework for nanomaterial identification to overcome the challenges. We demonstrate that our framework outperforms the popular baselines on the open-source datasets in nanomaterials-based identification tasks. The ablation studies are reported in great detail to support the efficacy of our approach.
@article{arxiv.2409.03767,
title = {EMCNet : Graph-Nets for Electron Micrographs Classification},
author = {Sakhinana Sagar Srinivas and Rajat Kumar Sarkar and Venkataramana Runkana},
journal= {arXiv preprint arXiv:2409.03767},
year = {2024}
}
Comments
12 pages, 10 figures, Accepted in a ACM SIGKDD 2022 Workshop on Machine Learning for Materials