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Measuring the thickness of contamination layers in scanning electron microscopy using image processing
Macek, Matěj ; Munzar, Milan (oponent) ; Čadík, Martin (vedoucí práce)
The motivation for this thesis arises from the aim of the Thermo Fisher Scientific company to develop a method to measure the thickness of contamination layers in scanning electron microscopy (SEM) images through advanced image processing techniques. The primary aim is to create automated methods for quantifying contamination in images that adversely impact imaging in material science research, using image processing techniques. In this study, we collect a dataset of images with contamination and manually annotate masks for each image. These annotations will serve to fine-tune and evaluate the effectiveness of the methods we propose. By employing a combination of edge detection algorithms and machine learning models, specifically a fine-tuned DeepLabv3 network, this work enhances the precision and efficiency for contamination detection. The edge Detection-Based Contamination Analyzer (EDCA) utilizes traditional image processing methods, while the DeepLabv3 model introduces a machine learning approach to robustly handle diverse imaging conditions. Comparative analyses demonstrate the effectiveness of these methods in providing reliable, scalable, and detailed measurements of contamination layers, significantly contributing to the field of materials science.
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