A Comparative Study of State-of-the-Art Deep Learning Models for Semantic Segmentation of Pores in Scanning Electron Microscope Images of Activated Carbon
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Updated
Mar 12, 2026 - Python
A Comparative Study of State-of-the-Art Deep Learning Models for Semantic Segmentation of Pores in Scanning Electron Microscope Images of Activated Carbon
Python-based automated image processing pipeline developed to predict mineral liberation behavior from polished section SEM images of chalcopyrite and pyrite.
Microscopic images of moss gathered in Northern Philadelphia, PA, USA using the Quanta 600 FEG Environmental Scanning Electron Microscope in the Nanoscale Characterization Facility at the Krishna P. Singh Center for Nanotechnology.
晶圆 SEM 缺陷分析桌面演示:分类、语义分割、形貌描述与检索流程;不含保密数据和检索图库。
AI-Based Restoration of Degraded Semiconductor Images | SEMICON India Hackathon 2026 | Multi-task deep learning with defect preservation
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