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Auflistung nach Autor:in "Schmid, Thomas"

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  • Konferenzbeitrag
    Canola seed or not? Autoencoder-based Anomaly Detection in AgriculturalSeedProduction
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Kukushkin, Maksim; Enders, Matthias; Kaschuba, Reinhard; Bogdan, Martin; Schmid, Thomas
    Analysing harvested seeds is a time-consuming task in the seed-producing industry. Automating this process has the potential to enhance and expedite agricultural seed production. In our study, we focus on differentiating Canola seeds from visually similar non-Canola seeds using computer vision techniques. Our approach utilises both RGB and hyperspectral images, captured by a specialised camera, to train separate autoencoder neural networks. By leveraging the high spatial resolution of RGB data and the high spectral resolution of hyperspectral data, we develop distinct models for Canola seed analysis, ensuring a comprehensive and robust assessment. The autoencoder networks are trained on a dataset of Canola seeds, allowing for the extraction of latent representations from both RGB and hyperspectral data. This enables efficient compression of input data and effective discrimination between Canola and non-Canola seeds. Our proposed approach demonstrates promising results in detecting non-Canola seeds in unseen test data.
  • Zeitschriftenartikel
    The AI Methods, Capabilities and Criticality Grid
    (KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Schmid, Thomas; Hildesheim, Wolfgang; Holoyad, Taras; Schumacher, Kinga
    Many artificial intelligence (AI) technologies developed over the past decades have reached market maturity and are now being commercially distributed in digital products and services. Therefore, national and international AI standards are currently being developed in order to achieve technical interoperability as well as reliability and transparency. To this end, we propose to classify AI applications in terms of the algorithmic methods used, the capabilities to be achieved and the level of criticality. The resulting three-dimensional classification scheme, termed the AI Methods, Capabilities and Criticality (AI- $$\hbox {MC}^2$$ MC 2 ) Grid, combines current recommendations of the EU Commission with an ethical dimension proposed by the Data Ethics Commission of the German Federal Government (Datenethikkommission der Bundesregierung: Gutachten. Berlin, 2019). As a whole, the AI- $$\hbox {MC}^2$$ MC 2 Grid allows not only to gain an overview of the implications of a given AI application as well as to compare efficiently different AI applications within a given market or implemented by different AI technologies. It is designed as a core tool to define and manage norms, standards and compliance of AI applications, but helps to manage AI solutions in general as well.