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activities:theme1:projects:std:index [2015/12/15 13:47]
janin
activities:theme1:projects:std:index [2015/12/22 15:52]
janin
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 Detecting tools in surgical videos is an important indregient for context-aware computer-assisted intervention systems. We propose a new two-stage pipeline for tool detection and pose estimation in 2d images, named ShapeDetector. Our approach is data-driven and overcomes strong assumptions made regarding the geometry, number, and position of tools in the image. Our method has been validated for the following three pose parameters: overall position, tip location, and orientaton; using a new surgical tool dataset: the NeuroSurgicalTools data-set made of 2476 monocular images from neurosurgical microscopes during in-vivo surgeries. Detecting tools in surgical videos is an important indregient for context-aware computer-assisted intervention systems. We propose a new two-stage pipeline for tool detection and pose estimation in 2d images, named ShapeDetector. Our approach is data-driven and overcomes strong assumptions made regarding the geometry, number, and position of tools in the image. Our method has been validated for the following three pose parameters: overall position, tip location, and orientaton; using a new surgical tool dataset: the NeuroSurgicalTools data-set made of 2476 monocular images from neurosurgical microscopes during in-vivo surgeries.
  
-{{ :​activities:​theme1:​representativeimage.png?​400 }}+[[https://​ecm.univ-rennes1.fr/​nuxeo/​nxdoc/​default/​55cab40f-6564-4026-99b5-37ffb10cdfb3/​view_documents|{{ :​activities:​theme1:​representativeimage.png?​400 }}]] 
 + 
 [[https://​ecm.univ-rennes1.fr/​nuxeo/​nxdoc/​default/​55cab40f-6564-4026-99b5-37ffb10cdfb3/​view_documents|**IMAGES AND ANNOTATIONS**]] [[https://​ecm.univ-rennes1.fr/​nuxeo/​nxdoc/​default/​55cab40f-6564-4026-99b5-37ffb10cdfb3/​view_documents|**IMAGES AND ANNOTATIONS**]]
  
 We provide separate train and test splits as long as corresponding annotations in the LabelMe format (one annotation file per image). ​ We provide separate train and test splits as long as corresponding annotations in the LabelMe format (one annotation file per image). ​
  
-[[http://​dbouget.bitbucket.org/​2015_tmi_surgical_tool_detection/​ +[[http://​dbouget.bitbucket.org/​2015_tmi_surgical_tool_detection/​|More info]] 
-|More info]]+
 ====== Main Collaborators ====== ====== Main Collaborators ======
  
-  * [[http://www.bic.mcgill.ca|PrLouis Collins MNI McGill University Canada]]+  * [[https://www.mpi-inf.mpg.de/​departments/​computer-vision-and-multimodal-computing/​|Rodrigo Benenson, Bernt Schiele, Max-Planck-Institut für Informatik, in Saarbrücken,​ Germany.]] 
 +  * Funding by Carl Zeiss, Germany 
inserm rennes1 ltsi