01634nam 2200409z- 450 9910346771103321202102121000054609(CKB)4920000000100812(oapen)https://directory.doabooks.org/handle/20.500.12854/59441(oapen)doab59441(EXLCZ)99492000000010081220202102d2016 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierSituation Interpretation for Knowledge- and Model Based Laparoscopic SurgeryKIT Scientific Publishing20161 online resource (XV, 148 p. p.)3-7315-0527-4 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.AssistanceAssistenzAugmented RealityChirurgieErweiterte RealitätMachine LearningMaschinelles LernenOntologieOntologySurgeryKati? Darkoauth1314082BOOK9910346771103321Situation Interpretation for Knowledge- and Model Based Laparoscopic Surgery3031682UNINA