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Synthetic training data generation for deep learning based quality inspection

Autor: Gutierrez, Pierre, Luschkova, Maria, Cordier, Antoine, Shukor, Mustafa, Schappert, Mona, Dahmen, Tim

Deep learning is now the gold standard in computer vision-based quality inspection systems. In order to detect defects, supervised learning is often utilized, but necessitates a large amount of annotated images, which can be costly: collecting, clean

Externí odkaz: http://arxiv.org/abs/2104.02980

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How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?

Autor: Trampert, Patrick, Bourghorbel, Faysal, Potocek, Pavel, Peemen, Maurice, Schlinkmann, Christian, Dahmen, Tim, Slusallek, Philipp

In scanning electron microscopy, the achievable image quality is often limited by a maximum feasible acquisition time per dataset. Particularly with regard to three-dimensional or large field-of-view imaging, a compromise must be found between a high

Externí odkaz: http://arxiv.org/abs/1801.04085

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Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting

Autor: Dahmen, Tim, Trampert, Patrick, Peter, Pascal, Bheed, Pinak, Weickert, Joachim, Slusallek, Philipp

Exemplar-based inpainting is the process of reconstructing missing parts of an image by searching the remaining data for patches that fit seamlessly. The image is completed to a plausible-looking solution by repeatedly inserting the patch that is the

Externí odkaz: http://arxiv.org/abs/1712.06326

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