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pro vyhledávání: '"Rosenhahn A"'
The evaluation of cell tracking results steers the development of tracking methods, significantly impacting biomedical research. This is quantitatively achieved by means of evaluation metrics. Unfortunately, current metrics favor local correctness an
Externí odkaz:
http://arxiv.org/abs/2408.11571
Autor:
Schubert, Frederik, Bethmann, Konrad, Mahlau, Yannik, Hartmann, Fabian, Caspary, Reinhard, Munderloh, Marco, Ostermann, Jörn, Rosenhahn, Bodo
The inverse design of photonic integrated circuits (PICs) presents distinctive computational challenges, including their large memory requirements. Advancements in the two-photon polymerization (2PP) fabrication process introduce additional complexit
Externí odkaz:
http://arxiv.org/abs/2407.10273
Describing a scene in terms of primitives -- geometrically simple shapes that offer a parsimonious but accurate abstraction of structure -- is an established vision problem. This is a good model of a difficult fitting problem: different scenes requir
Externí odkaz:
http://arxiv.org/abs/2405.19569
Detecting anomalies in images has become a well-explored problem in both academia and industry. State-of-the-art algorithms are able to detect defects in increasingly difficult settings and data modalities. However, most current methods are not suite
Externí odkaz:
http://arxiv.org/abs/2404.06832
Cell tracking and segmentation assist biologists in extracting insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches often suffer from a lack of long-term consistency. To address this issu
Externí odkaz:
http://arxiv.org/abs/2403.15011
Publikováno v:
2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Recently, regression-based methods have dominated the field of 3D human pose and shape estimation. Despite their promising results, a common issue is the misalignment between predictions and image observations, often caused by minor joint rotation er
Externí odkaz:
http://arxiv.org/abs/2403.11634
Multi-class multi-instance segmentation is the task of identifying masks for multiple object classes and multiple instances of the same class within an image. The foundational Segment Anything Model (SAM) is designed for promptable multi-class multi-
Externí odkaz:
http://arxiv.org/abs/2403.10780
Humans perceive and construct the world as an arrangement of simple parametric models. In particular, we can often describe man-made environments using volumetric primitives such as cuboids or cylinders. Inferring these primitives is important for at
Externí odkaz:
http://arxiv.org/abs/2403.10452
The combination of self-play and planning has achieved great successes in sequential games, for instance in Chess and Go. However, adapting algorithms such as AlphaZero to simultaneous games poses a new challenge. In these games, missing information
Externí odkaz:
http://arxiv.org/abs/2402.03136
Autor:
Rosenhahn, Bodo, Hirche, Christoph
A Normalizing Flow computes a bijective mapping from an arbitrary distribution to a predefined (e.g. normal) distribution. Such a flow can be used to address different tasks, e.g. anomaly detection, once such a mapping has been learned. In this work
Externí odkaz:
http://arxiv.org/abs/2402.02866