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- author = {Gedik, O Serdar and Alatan, a Aydn},
- doi = {10.1109/TCYB.2013.2272735},
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- mendeley-groups = {Databases},
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- keywords = {3D depth cameras,CMOS image sensor,CMOS image sensors,Cameras,Games,IR projector,Kinect sensor vision aspect,Microsoft Kinect,Microsoft Kinect sensor,Sensors,Three dimensional displays,Video recording,advanced sensing hardware,computer vision,gaming industry,human computer interaction,human-computer interaction,infrared detectors,infrared projector,motion capture,multimedia,multimedia computing,optical projectors},
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- doi = {10.1109/ICCVW.2011.6130391},
- file = {:home/ope/Mendeley Desktop/Keskin et al. - 2011 - Real time hand pose estimation using depth sensors.pdf:pdf},
- keywords = {3D hand model,Accuracy,American sign language digit recognition,Estimation,Joints,Kinect,SVM based recognition module,Three dimensional displays,Training,Vegetation,depth sensors,image classification,object recognition,per pixel classification,pose estimation,random decision forests,real time hand pose estimation,real-time skeleton fitting,support vector machine,support vector machines,synthetic depth images},
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- isbn = {1063-6919 VO -},
- keywords = {3D positions,Cameras,Joints,Proposals,Shape,Three dimensional displays,Training,Vegetation,confidence-scored 3D proposals,image classification,image resolution,intermediate body parts representation,object recognition,object recognition approach,pose estimation,pose estimation problem,real-time human pose recognition,simpler per-pixel classification problem,single depth image,whole-skeleton nearest neighbor matching},
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- keywords = {3D color datasets,3D color texture features,3D points,3D space,Feature extraction,Image color analysis,Image segmentation,Joints,Microsoft Kinect,Three dimensional displays,Training,Vectors,candidate segments,consumer cameras,depth images,geometry,geometry-based segmentation,global 3D feature extraction,image classification,image colour analysis,image texture,learning (artificial intelligence),multiple instance learning,object classification,object detection,object labels,object localization,object segments,rotation invariant color features,scale invariance,spatial discontinuity,surface normals,view-point variance,weakly-supervised object learning},
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- file = {:home/ope/Mendeley Desktop/Wohlkinger, Vincze - 2011 - Shape distributions on voxel surfaces for 3D object classification from depth images.pdf:pdf},
- keywords = {3D model retrieval,3D object classification,3D shape based object class recognition,3D shape descriptor,Adaptation models,CAD,CAD model database,Databases,Histograms,Internet,RGB-D cameras,Robot sensing systems,Shape,Solid modeling,Three dimensional displays,cameras,depth image,home robotic domain,image classification,image coding,image matching,image retrieval,matching problem,object manipulation,object recognition,point cloud data,sensitive hashing,shape recognition,solid modelling,visual databases,voxel surface shape distribution},
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- year = {2011}
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