LEADER 01634nam  2200409z- 450 
001 9910346771103321
005 20210212
010   $a1000054609
035   $a(CKB)4920000000100812
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/59441
035   $a(oapen)doab59441
035   $a(EXLCZ)994920000000100812
100   $a20202102d2016      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aSituation Interpretation for Knowledge- and Model Based Laparoscopic Surgery
210   $cKIT Scientific Publishing$d2016
215   $a1 online resource (XV, 148 p. p.)
311 08$a3-7315-0527-4 
330   $aTo 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.
610   $aAssistance
610   $aAssistenz
610   $aAugmented Reality
610   $aChirurgie
610   $aErweiterte Realita?tMachine Learning
610   $aMaschinelles Lernen
610   $aOntologie
610   $aOntology
610   $aSurgery
700   $aKati?$b Darko$4auth$01314082
906   $aBOOK
912   $a9910346771103321
996   $aSituation Interpretation for Knowledge- and Model Based Laparoscopic Surgery$93031682
997   $aUNINA