LEADER 05526nam 22008415 450 001 9910483256403321 005 20251226195905.0 010 $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 $eDAGM 2009 Workshop, Dyn3D 2009, Jena, Germany, September 9, 2009, Proceedings /$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 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v5742 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$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,$x3004-9954 ;$v5742 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aPattern recognition systems 606 $aComputer graphics 606 $aInformation storage and retrieval systems 606 $aData mining 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputer Vision 606 $aAutomated Pattern Recognition 606 $aComputer Graphics 606 $aInformation Storage and Retrieval 606 $aData Mining and Knowledge Discovery 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 0$aComputer graphics. 615 0$aInformation storage and retrieval systems. 615 0$aData mining. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputer Vision. 615 24$aAutomated Pattern Recognition. 615 24$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 701 $aKolb$b Andreas$01754274 701 $aKoch$b Reinhard$01754275 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483256403321 996 $aDynamic 3d imaging$94190520 997 $aUNINA LEADER 03597oas 2201405 a 450 001 9910146485103321 005 20260127110453.0 011 $a1600-0668 035 $a(DE-599)ZDB2028169-9 035 $a(OCoLC)47806196 035 $a(CONSER) 2001229225 035 $a(CKB)954925562609 035 $a(DE-599)2028169-9 035 $a(EXLCZ)99954925562609 100 $a20010820a19919999 sy a 101 0 $aeng 135 $aurmnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aIndoor air 210 $aCopenhagen $cDanish Technical Press 210 2 $aCopenhagen $cMunksgaard 210 2 $aRahway, NJ $cJohn Wiley & Son, Inc 210 31$aFrederiksberg, Denmark :$cJohn Wiley & Sons Ltd 215 $a1 online resource 300 $aRefereed/Peer-reviewed 300 $aArticles published as they are received and compiled into annual volumes. 311 08$a0905-6947 531 0 $aIndoor air 606 $aIndoor air pollution$vPeriodicals 606 $aSick building syndrome$vPeriodicals 606 $aVentilation$vPeriodicals 606 $aAir Pollution, Indoor 606 $aAir Quality Management$2ebps 606 $aIndoor air pollution$2fast$3(OCoLC)fst00970672 606 $aSick building syndrome$2fast$3(OCoLC)fst01118063 606 $aVentilation$2fast$3(OCoLC)fst01165252 606 $aPollution inte?rieure$2rasuqam 606 $aSyndrome des tours a? bureaux$2rasuqam 606 $aVentilation$2rasuqam 606 $aImmeuble de bureaux$2rasuqam 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 608 $aPe?riodique e?lectronique (Descripteur de forme)$2rasuqam 608 $aRessource Internet (Descripteur de forme)$2rasuqam 615 0$aIndoor air pollution 615 0$aSick building syndrome 615 0$aVentilation 615 2$aAir Pollution, Indoor. 615 7$aAir Quality Management. 615 7$aIndoor air pollution. 615 7$aSick building syndrome. 615 7$aVentilation. 615 7$aPollution inte?rieure. 615 7$aSyndrome des tours a? bureaux. 615 7$aVentilation. 615 7$aImmeuble de bureaux. 676 $a613.5 676 $a628.53 712 02$aInternational Society of Indoor Air Quality and Climate, 801 0$bKKU 801 1$bKKU 801 2$bFUG 801 2$bOCLCQ 801 2$bF#A 801 2$bOCLCQ 801 2$bHNK 801 2$bOCL 801 2$bUQ1 801 2$bOCLCQ 801 2$bBUF 801 2$bCUD 801 2$bTXJ 801 2$bCO3 801 2$bLGG 801 2$bOCLCQ 801 2$bCOO 801 2$bUBF 801 2$bOCLCF 801 2$bVT2 801 2$bNLGGC 801 2$bOCLCQ 801 2$bDLC 801 2$bOCLCO 801 2$bOCLCQ 801 2$bZ5A 801 2$bOCLCO 801 2$bWT2 801 2$bNLE 801 2$bOCLCA 801 2$bNJR 801 2$bU3W 801 2$bEZC 801 2$bOCLCQ 801 2$bOCLCO 801 2$bWYU 801 2$bUKMGB 801 2$bOCLCO 801 2$bOCLCA 801 2$bOCLCQ 801 2$bOCLCO 801 2$bNJT 801 2$bSFB 801 2$bOCLCA 801 2$bOCLCQ 801 2$bLDP 801 2$bCNMTR 801 2$bSRU 801 2$bGUA 801 2$bUBY 801 2$bOCLCQ 801 2$bOCLCL 801 2$bDLC 801 2$bOCLCL 801 2$bOCLCQ 906 $aJOURNAL 912 $a9910146485103321 996 $aIndoor air$92173521 997 $aUNINA