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EMCNet : Graph-Nets for Electron Micrographs Classification

arXiv 2409.03767 ↗First posted 21 Aug 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 43 of its 57 references that are in the map. First posted on arXiv 21 Aug 2024.

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References · 43

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