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Journal Article Deep-Learning Based Autofocus Score Prediction of Scanning Electron Microscope Get access Huisoo Kim, Huisoo Kim Egovid Inc., UNIST-gil 50, Ulsan 44919, Korea Search for other works by this author on: Oxford Academic Google Scholar Moohyun Oh, Moohyun Oh Egovid Inc., UNIST-gil 50, Ulsan 44919, Korea Search for other works by this author on: Oxford Academic Google Scholar Heerang Lee, Heerang Lee Egovid Inc., UNIST-gil 50, Ulsan 44919, Korea Search for other works by this author o

Hyun Jong Yang, Moohyun Oh, Jonggyu Jang, Hyeonsu Lyu, Junhee Lee; Robust Deep-learning Based Autofocus Score Prediction for Scanning Electron Microscope,

By virtue of their ultra high resolution, scanning electron microscopes (SEMs) are essential to study topography, morphology, composition, and crystallography of materials, and thus are widely used for advanced researches in physics, chemistry, pharmacy, geology, etc. The major hindrance of using SEMs is that obtaining high quality images from SEMs requires a professional control of many control parameters. Therefore, it is not an easy task even for an experienced researcher to get high quality

The process of drawing digital comics and animations is a complex process that involves multiple stages. Flat-coloring, the task of filling segmented regions in a line art image with uniform tone and hue, is a particularly time-consuming and labor-intensive task. We have identified that artists suffer from not only adjusting colors in overflowing regions due to line discontinuity but also finding to replace misaligned pixels near the line due to region-bleeding problems (aliasing issues). To add

In this letter, we propose a novel, fast model for detecting fire flames and smoke using object detection and 3D classification, referred to as FastFireDet3D. This model uses NanoDet to quickly identify potential areas representing fire and smoke, followed by a novel 3D classification model based on a spatio-temporal convolutional neural network (STCNN). This two-step process allows for efficient and accurate detection. The average processing time for FastFireDet3D is approximately 40-90 ms when