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01612nma a2200349 u 4500 |
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210512 ||| eng |
| 020 |
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|a 9783731505273
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| 020 |
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|a 1000054609
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| 100 |
1 |
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|a Kati?, Darko
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| 245 |
0 |
0 |
|a Situation Interpretation for Knowledge- and Model Based Laparoscopic Surgery
|h Elektronische Ressource
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| 260 |
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|b KIT Scientific Publishing
|c 2016
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| 300 |
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|a 1 electronic resource (XV, 148 p. p.)
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| 653 |
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|a Ontology
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| 653 |
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|a Chirurgie
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| 653 |
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|a Maschinelles Lernen
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| 653 |
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|a Augmented Reality
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| 653 |
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|a Surgery
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| 653 |
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|a Assistenz
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| 653 |
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|a Ontologie
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| 653 |
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|a Erweiterte RealitätMachine Learning
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| 653 |
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|a Assistance
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-sa/4.0/
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| 028 |
5 |
0 |
|a 10.5445/KSP/1000054609
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/59441
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://www.ksp.kit.edu/9783731505273
|7 0
|x Verlag
|3 Volltext
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| 520 |
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|a To manage the influx of information into surgical practice, new man-machine interaction methods are necessary to prevent information overflow. This work presents an approach to automatically segment surgeries into phases and select the most appropriate pieces of information for the current situation. This way, assistance systems can adopt themselves to the needs of the surgeon and not the other way around.
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