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020 _a9781447146407
_9978-1-4471-4640-7
024 7 _a10.1007/978-1-4471-4640-7
_2doi
050 4 _aTA1637-1638
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aUYQV
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072 7 _aCOM012000
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072 7 _aCOM016000
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082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
245 1 0 _aConsumer Depth Cameras for Computer Vision
_h[electronic resource] :
_bResearch Topics and Applications /
_cedited by Andrea Fossati, Juergen Gall, Helmut Grabner, Xiaofeng Ren, Kurt Konolige.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aXVI, 210 p. 109 illus., 106 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
505 0 _aPart I: 3D Registration and Reconstruction -- 3D with Kinect -- Real-Time RGB-D Mapping and 3-D Modeling on the GPU using the Random Ball Cover -- A Brute Force Approach to Depth Camera Odometry -- Part II: Human Body Analysis -- Key Developments in Human Pose Estimation for Kinect -- A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera -- Home 3D Body Scans from a Single Kinect -- Real-Time Hand Pose Estimation using Depth Sensors -- Part III: RGB-D Datasets -- A Category-Level 3D Object Dataset: Putting the Kinect to Work -- RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark -- RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition.
520 _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.
650 0 _aComputer science.
650 0 _aComputer graphics.
650 0 _aImage processing.
650 0 _aPattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aPattern Recognition.
650 2 4 _aComputer Graphics.
700 1 _aFossati, Andrea.
_eeditor.
700 1 _aGall, Juergen.
_eeditor.
700 1 _aGrabner, Helmut.
_eeditor.
700 1 _aRen, Xiaofeng.
_eeditor.
700 1 _aKonolige, Kurt.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447146391
830 0 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-4640-7
912 _aZDB-2-SCS
942 _cEBK
999 _c56350
_d56350