LEADER 03606nam 2200637 a 450 001 9910437600903321 005 20200520144314.0 010 $a1-283-90966-9 010 $a1-4471-4640-9 024 7 $a10.1007/978-1-4471-4640-7 035 $a(CKB)2670000000277594 035 $a(EBL)1081753 035 $a(OCoLC)813213128 035 $a(SSID)ssj0000766971 035 $a(PQKBManifestationID)11423965 035 $a(PQKBTitleCode)TC0000766971 035 $a(PQKBWorkID)10732523 035 $a(PQKB)10598752 035 $a(DE-He213)978-1-4471-4640-7 035 $a(MiAaPQ)EBC1081753 035 $a(PPN)168293986 035 $a(EXLCZ)992670000000277594 100 $a20121008d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aConsumer depth cameras for computer vision $eresearch topics and applications /$fAndrea Fossati ... [et al.], editors 205 $a1st ed. 2013. 210 $aLondon $cSpringer$d2013 215 $a1 online resource (219 p.) 225 0$aAdvances in computer vision and pattern recognition,$x2191-6586 300 $aDescription based upon print version of record. 311 $a1-4471-4639-5 320 $aIncludes bibliographical references and index. 327 $apt. 1. 3D registration and reconstruction -- pt. 2. Human body analysis -- pt. 3. RGB-D datasets. 330 $aThe launch of Microsoft?s Kinect, the first high-resolution depth-sensing camera for the consumer market, generated considerable excitement not only among computer gamers, but also within the global community of computer vision researchers. The potential of consumer depth cameras extends well beyond entertainment and gaming, to real-world commercial applications such virtual fitting rooms, training for athletes, and assistance for the elderly. This authoritative text/reference reviews the scope and impact of this rapidly growing field, describing the most promising Kinect-based research activities, discussing significant current challenges, and showcasing exciting applications. Topics and features: Presents contributions from an international selection of preeminent authorities in their fields, from both academic and corporate research Addresses the classic problem of multi-view geometry of how to correlate images from different viewpoints to simultaneously estimate camera poses and world points Examines human pose estimation using video-rate depth images for gaming, motion capture, 3D human body scans, and hand pose recognition for sign language parsing Provides a review of approaches to various recognition problems, including category and instance learning of objects, and human activity recognition With a Foreword by Dr. Jamie Shotton of Microsoft Research, Cambridge, UK This broad-ranging overview is a must-read for researchers and graduate students of computer vision and robotics wishing to learn more about the state of the art of this increasingly ?hot? topic. 410 0$aAdvances in Computer Vision and Pattern Recognition,$x2191-6586 606 $aComputer vision 606 $aPhotogrammetry 606 $aCameras$xCalibration 615 0$aComputer vision. 615 0$aPhotogrammetry. 615 0$aCameras$xCalibration. 676 $a006.3 676 $a006.3/7 676 $a006.37 701 $aFossati$b Andrea$0267736 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437600903321 996 $aConsumer depth cameras for computer vision$94196632 997 $aUNINA