EMCNet : Graph-Nets for Electron Micrographs Classification

• URL: http://arxiv.org/abs/2409.03767v2
• Date: Tue, 10 Sep 2024 13:23:57 GMT
• Title: EMCNet : Graph-Nets for Electron Micrographs Classification
• Authors: Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana,
• Abstract summary: We propose an effective end-to-end electron micrograph representation learning-based framework for nanomaterial identification. We demonstrate that our framework outperforms the popular baselines on the open-source datasets in nanomaterials-based identification tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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