@misc{Jezierska_Anna_Instrument_2017,
 author={Jezierska, Anna and Węsierski, Daniel},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Raport Badawczy = Research Report},
 howpublished={online},
 year={2017},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={In this paper, a new method for detection and pose estimation of multiple nonrigid and robotic tools in surgical videos was introduced. The method uses a rigidly structured, bipartite model of end-effector and shaft parts that consistently encode diverse, pose-specific appearance mixtures of the tool. This rigid part mixtures model then jointly explains the evolving tool structure by switching between mixture components.The effective procedure for learning such rigid mixtures from videos and for pooling the modeled shaft part that undergoes frequent truncation at the border of the imaged scene is proposed. Experiments further illustrate that estimation of end-effector pose improves upon including the shaft part in the model.},
 type={Text},
 title={Instrument detection and pose estimation with rigid part mixtures model in video-assisted surgeries},
 URL={http://www.rcin.org.pl/Content/144865/PDF/RB-2017-23.pdf},
 keywords={Surgical instrument detection, Wykrywanie narzędzi chirurgicznych, Surgical instrument tracking, śledzenie narzędzi chirurgicznych, Video-assisted minimally invasive surgery, Minimalnie inwazyjna operacja wspomagana wideo, Robotic surgery, Chirurgia robotyczna, Part-based models, Modele oparte na częściach},
}