Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Lars Schmarje"'
Publikováno v:
IEEE Access, Vol 9, Pp 82146-82168 (2021)
While deep learning strategies achieve outstanding results in computer vision tasks, one issue remains: The current strategies rely heavily on a huge amount of labeled data. In many real-world problems, it is not feasible to create such an amount of
Externí odkaz:
https://doaj.org/article/19eacb9697b84a03ab19befee1c03787
Autor:
Lars Schmarje, Johannes Brünger, Monty Santarossa, Simon-Martin Schröder, Rainer Kiko, Reinhard Koch
Publikováno v:
Sensors, Vol 21, Iss 19, p 6661 (2021)
Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in sem
Externí odkaz:
https://doaj.org/article/f46ca07e0a63421bb7a7eec7f858b666
Autor:
Monty Santarossa, Ayse Kilic, Claus von der Burchard, Lars Schmarje, Claudius Zelenka, Stefan Reinhold, Reinhard Koch, Johann Roider
Publikováno v:
Medical Imaging 2022: Image Processing.
Publikováno v:
Predictive Intelligence in Medicine ISBN: 9783031169182
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99a16cb9a303ada453197b6fad5631c5
https://doi.org/10.1007/978-3-031-16919-9_10
https://doi.org/10.1007/978-3-031-16919-9_10
Autor:
Monty Santarossa, Claudius Zelenka, Uwe Franke, Lukas Schneider, Lars Schmarje, Reinhard Koch
Publikováno v:
2021 IEEE Intelligent Vehicles Symposium (IV).
Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation. However, due to their sparse occurrence in the image, until now Stixels seldomly served as input for Deep Learning
Publikováno v:
Theory of Computing Systems. 64:120-140
We study the Parallel Task Scheduling problem $Pm|size_j|C_{\max}$ with a constant number of machines. This problem is known to be strongly NP-complete for each $m \geq 5$, while it is solvable in pseudo-polynomial time for each $m \leq 3$. We give a
Publikováno v:
IEEE Access, Vol 9, Pp 82146-82168 (2021)
While deep learning strategies achieve outstanding results in computer vision tasks, one issue remains: The current strategies rely heavily on a huge amount of labeled data. In many real-world problems, it is not feasible to create such an amount of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::534ee864a5eae7e24a257fd5a1117b99
http://arxiv.org/abs/2002.08721
http://arxiv.org/abs/2002.08721
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030336752
GCPR
GCPR
Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2c6ac43281f3319817bb526b548096d
https://doi.org/10.1007/978-3-030-33676-9_26
https://doi.org/10.1007/978-3-030-33676-9_26
Publikováno v:
Computer Science – Theory and Applications ISBN: 9783319905297
CSR
CSR
We study Parallel Task Scheduling \(Pm|size_j|C_{\max }\) with a constant number of machines. This problem is known to be strongly NP-complete for each \(m \ge 5\), while it is solvable in pseudo-polynomial time for each \(m \le 3\). We give a positi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b26c35b3a497457ee53492d01e6d067
https://doi.org/10.1007/978-3-319-90530-3_15
https://doi.org/10.1007/978-3-319-90530-3_15