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arXiv · 2409.03767

EMCNet : Graph-Nets for Electron Micrographs Classification

Sakhinana Sagar Srinivas·Rajat Kumar Sarkar·Venkataramana Runkana
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Abstract

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.

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Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana. 2024-08-21. EMCNet : Graph-Nets for Electron Micrographs Classification. https://arxiv.org/abs/2409.03767

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