Zobrazeno 1 - 10
of 213
pro vyhledávání: '"Schmid Ute"'
Autor:
Mohammed Aliya, Geppert Carol, Hartmann Arnd, Kuritcyn Petr, Bruns Volker, Schmid Ute, Wittenberg Thomas, Benz Michaela, Finzel Bettina
Publikováno v:
Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 229-232 (2022)
Deep Learning-based tissue classification may support pathologists in analyzing digitized whole slide images. However, in such critical tasks, only approaches that can be validated by medical experts in advance to deployment, are suitable. We present
Externí odkaz:
https://doaj.org/article/74eb23bf620942ceb0023d7608d7c4cb
Being able to recognise defects in industrial objects is a key element of quality assurance in production lines. Our research focuses on visual anomaly detection in RGB images. Although Convolutional Neural Networks (CNNs) achieve high accuracies in
Externí odkaz:
http://arxiv.org/abs/2410.12817
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and se
Externí odkaz:
http://arxiv.org/abs/2408.06190
Autor:
Bahr, Lukas, Wehner, Christoph, Wewerka, Judith, Bittencourt, José, Schmid, Ute, Daub, Rüdiger
Failure mode and effects analysis (FMEA) is a critical tool for mitigating potential failures, particular during ramp-up phases of new products. However, its effectiveness is often limited by the missing reasoning capabilities of the FMEA tools, whic
Externí odkaz:
http://arxiv.org/abs/2406.18114
Recent advancements in generative AI have introduced novel prospects and practical implementations. Especially diffusion models show their strength in generating diverse and, at the same time, realistic features, positioning them well for generating
Externí odkaz:
http://arxiv.org/abs/2406.01649
Ensuring the quality of black-box Deep Neural Networks (DNNs) has become ever more significant, especially in safety-critical domains such as automated driving. While global concept encodings generally enable a user to test a model for a specific con
Externí odkaz:
http://arxiv.org/abs/2405.17523
Explanations for Convolutional Neural Networks (CNNs) based on relevance of input pixels might be too unspecific to evaluate which and how input features impact model decisions. Especially in complex real-world domains like biology, the presence of s
Externí odkaz:
http://arxiv.org/abs/2405.01661
The goal of inductive program synthesis is for a machine to automatically generate a program from user-supplied examples of the desired behaviour of the program. A key underlying assumption is that humans can provide examples of sufficient quality to
Externí odkaz:
http://arxiv.org/abs/2404.19397
Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since
Externí odkaz:
http://arxiv.org/abs/2404.03499