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Titlebook: Bildverarbeitung in der Automation; Ausgew?hlte Beitr?ge Volker Lohweg Conference proceedings‘‘‘‘‘‘‘‘ 2023 Der/die Herausgeber bzw. der/die

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21#
發(fā)表于 2025-3-25 04:54:02 | 只看該作者
22#
發(fā)表于 2025-3-25 10:53:47 | 只看該作者
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發(fā)表于 2025-3-25 12:57:01 | 只看該作者
Advanced Feature Extraction Workflow for Few Shot Object Recognition,achieve an object recognition approach by eliminating the “color blindness” of key point extraction methods by using a?combination of SIFT, color histograms and contour detection algorithms. This approach is evaluated in context of object recognition on a?conveyor belt. In this scenario, objects can
24#
發(fā)表于 2025-3-25 17:27:16 | 只看該作者
A Study on Data Augmentation Techniques for Visual Defect Detection in Manufacturing,ure a?set of random DA-pipelines to generate datasets of different characteristics. To investigate the impact of DA-techniques on defect detection performance, we then train convolutional neural networks with two different but fixed architectures and hyperparameter sets. To quantify and evaluate the
25#
發(fā)表于 2025-3-25 22:51:08 | 只看該作者
Creating Synthetic Training Data for Machine Vision Quality Gates,ow that synthetically generated training data used to train machine vision quality gates is fundamentally suitable. This offers great potential to relieve process and productions developers in the development of quality gates in the future.
26#
發(fā)表于 2025-3-26 02:01:57 | 只看該作者
27#
發(fā)表于 2025-3-26 04:38:45 | 只看該作者
28#
發(fā)表于 2025-3-26 10:48:14 | 只看該作者
?Ihr seid die beste Gemeinde“ (3:110)ine Identifizierung der Personen. Hierfür wird ein Verfahren vorgestellt, das einzelne Objekte in einer Punktwolke zun?chst in ein Tiefenbild umwandelt, um auf diesem anschlie?end robuste Bildverarbeitungsverfahren basierend auf Deep Learning einzusetzen. Die Evaluation des Verfahrens zeigt eine Gen
29#
發(fā)表于 2025-3-26 15:26:44 | 只看該作者
30#
發(fā)表于 2025-3-26 20:08:58 | 只看該作者
Islam’s Marriage with Neoliberalismure a?set of random DA-pipelines to generate datasets of different characteristics. To investigate the impact of DA-techniques on defect detection performance, we then train convolutional neural networks with two different but fixed architectures and hyperparameter sets. To quantify and evaluate the
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