LEADER 05891nam 22007815 450 001 996465844603316 005 20200704060339.0 010 $a3-642-03778-X 024 7 $a10.1007/978-3-642-03778-8 035 $a(CKB)1000000000798301 035 $a(SSID)ssj0000317296 035 $a(PQKBManifestationID)11231248 035 $a(PQKBTitleCode)TC0000317296 035 $a(PQKBWorkID)10292889 035 $a(PQKB)11112279 035 $a(DE-He213)978-3-642-03778-8 035 $a(MiAaPQ)EBC3064524 035 $a(PPN)13995516X 035 $a(EXLCZ)991000000000798301 100 $a20100301d2009 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aDynamic 3D Imaging$b[electronic resource] $eDAGM 2009 Workshop, Dyn3D 2009, Jena, Germany, September 9, 2009, Proceedings /$fedited by Andreas Kolb, Reinhard Koch 205 $a1st ed. 2009. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2009. 215 $a1 online resource (X, 177 p.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v5742 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-03777-1 320 $aIncludes bibliographical references and index. 327 $aFundamentals of ToF-Sensors -- A Physical Model of Time-of-Flight 3D Imaging Systems, Including Suppression of Ambient Light -- Compensation of Motion Artifacts for Time-of-Flight Cameras -- Radiometric and Spectrometric Calibrations, and Distance Noise Measurement of ToF Cameras -- Algorithms and Data Fusion -- Datastructures for Capturing Dynamic Scenes with a Time-of-Flight Camera -- Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking -- Depth Imaging by Combining Time-of-Flight and On-Demand Stereo -- Realistic Depth Blur for Images with Range Data -- Global Context Extraction for Object Recognition Using a Combination of Range and Visual Features -- Shadow Detection in Dynamic Scenes Using Dense Stereo Information and an Outdoor Illumination Model -- Applications of Dynamic 3D Scene Analysis -- MixIn3D: 3D Mixed Reality with ToF-Camera -- Self-Organizing Maps for Pose Estimation with a Time-of-Flight Camera -- Analysis of Gait Using a Treadmill and a Time-of-Flight Camera -- Face Detection Using a Time-of-Flight Camera. 330 $a3D imaging sensors have been investigated for several decades. Recently, - provements on classical approaches such as stereo vision and structured light on the one hand, and novel time-of-?ight (ToF) techniques on the other hand have emerged, leading to 3D vision systems with radically improvedcharacter- tics. Presently, these techniques make full-range 3D data available at interactive frame rates, and thus open the path toward a much broader application of 3D vision systems. The workshop on Dynamic 3D Vision (Dyn3D) was held in conjunction with the annual conference of the German Association of Pattern Recognition (DAGM) in Jena on September 9, 2009. Previous workshops in this series have focused on the same topic, i.e., the Dynamic 3D Vision workshopin conjunction with the DAGM conference in 2007 and the CVPR workshop Time of Flight Camera-Based Computer Vision (TOF-CV) in 2008. The goal of this year?s workshop, as for the prior events, was to constitute a platform for researchers working in the ?eld of real-time range imaging, where all aspects, from sensor evaluation to application scenarios, are addressed. After a very competitive and high-quality reviewing process, 13 papers were accepted for publication in this LNCS issue. The research area on dynamic 3D vision proved to be extremely lively. Again, as for prior workshops on this ?eld, numerous new insights and novel approaches on time-of-?ight sensors, on re- time mono- and multidimensional data processing and on various applications are presented in these workshop proceedings. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v5742 606 $aOptical data processing 606 $aPattern recognition 606 $aComputer graphics 606 $aInformation storage and retrieval 606 $aData mining 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aComputer graphics. 615 0$aInformation storage and retrieval. 615 0$aData mining. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aComputer Graphics. 615 24$aInformation Storage and Retrieval. 615 24$aData Mining and Knowledge Discovery. 676 $a621.36/7 686 $aDAT 757f$2stub 686 $aDAT 758f$2stub 686 $aSS 4800$2rvk 702 $aKolb$b Andreas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKoch$b Reinhard$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996465844603316 996 $aDynamic 3D Imaging$9773699 997 $aUNISA