Výsledky vyhledávání - "Marko Ristin"

Semi-automatic testing of data-focused software development kits for Industrie 4.0

Autor: Torben Miny, Sebastian Heppner, Igor Garmaev, Tobias Kleinert, Marko Ristin, Hans Wernher Van De Venn, Bjorn Otto, Karsten Meinecke, Christian Diedrich, Nico Braunisch, Martin Wollschlaeger

The digital twin, or more precisely the Asset Administration Shell, is the key element for interoperability in Industrie 4.0. Together with the asset, it forms the I4.0 component. For the development of I4.0 components, the availability of suitable s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a11f1ca82061b3da5d915f483953f25
https://hdl.handle.net/11475/27080

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Generative and model-driven SDK development for the industrie 4.0 digital twin

Autor: Robert Lehmann, Hans Wernher van de Venn, Marko Ristin-Kaufmann, Nico Braunisch

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5a4c52b33f9808cb034340bc7dbf32e
https://hdl.handle.net/11475/23646

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RASAECO: Requirements Analysis of Software for the AECO Industry

Autor: Hans Wernher van de Venn, Marko Ristin, Dag Fjeld Edvardsen

Publikováno v: RE
Datacite
ZHAW digitalcollection
UnpayWall
Microsoft Academic Graph
Sygma
arXiv.org e-Print Archive

Comment: Pre-print of our submission to 29th IEEE International Requirements Engineering Conference
Digitalization is forging its path in the architecture, construction, engineering, operation (AECO) industry. This trend demands not only solutio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b14b23cc7e61ba1e43ca0dd158589c8e
https://doi.org/10.1109/re51729.2021.00032

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Incremental Learning of Random Forests for Large-Scale Image Classification

Autor: Matthieu Guillaumin, Marko Ristin, Juergen Gall, Luc Van Gool

Publikováno v: IEEE transactions on pattern analysis and machine intelligence. 38(3)

Large image datasets such as ImageNet or open-ended photo websites like Flickr are revealing new challenges to image classification that were not apparent in smaller, fixed sets. In particular, the efficient handling of dynamically growing datasets,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::483d57528d5aec868420b4400a5228a6
https://pubmed.ncbi.nlm.nih.gov/27046493

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From categories to subcategories: Large-scale image classification with partial class label refinement

Autor: Juergen Gall, Marko Ristin, Luc Van Gool, Matthieu Guillaumin

Publikováno v: CVPR
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN:978-1-4673-6964-0
ISBN:978-1-4673-6963-3
ISBN:978-1-4673-6965-7

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b853b78e80a2888de5bfc08f8449997
https://doi.org/10.1109/cvpr.2015.7298619

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Discriminative learning of apparel features

Autor: Matthias Dantone, Rasmus Rothe, Marko Ristin, Luc Van Gool

Publikováno v: 2015 14th IAPR International Conference on Machine Vision Applications (MVA)
MVA

2015 14th IAPR International Conference on Machine Vision Applications (MVA)
ISBN:978-4-901122-14-6

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c966c9120d5423e1e405487f4a2a8d4
https://lirias.kuleuven.be/handle/123456789/532396

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Incremental learning of NCM forests for large-scale image classification

Autor: Marko Ristin, Juergen Gall, Matthieu Guillaumin, Luc Van Gool

Publikováno v: CVPR

© 2014 IEEE. In recent years, large image data sets such as 'ImageNet', 'TinyImages' or ever-growing social networks like 'Flickr' have emerged, posing new challenges to image classification that were not apparent in smaller image sets. In particula

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::906d675f3cc5d2766f740296fb2ad600
https://lirias.kuleuven.be/handle/123456789/485349

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Local context priors for object proposal generation

Autor: Juergen Gall, Luc Van Gool, Marko Ristin

Publikováno v: Computer Vision – ACCV 2012 ISBN: 9783642373305
ACCV (1)

State-of-the-art methods for object detection are mostly based on an expensive exhaustive search over the image at different scales. In order to reduce the computational time, one can perform a selective search to obtain a small subset of relevant ob

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98c15eb6cc7f6ebe41dbdf26b23d5a10
https://lirias.kuleuven.be/handle/123456789/377379

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