000			04141nam a22004215i 4500
001			286868
003			MX-SnUAN
005			20170705134214.0
007			cr nn 008mamaa
008			150903s2013 xxk| o |||| 0|eng d
020			_a9781447146407 _99781447146407
024	7		_a10.1007/9781447146407 _2doi
035			_avtls000339881
039		9	_a201509030320 _bVLOAD _c201404300406 _dVLOAD _y201402061011 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTA1637-1638
100	1		_aFossati, Andrea. _eeditor. _9315786
245	1	0	_aConsumer Depth Cameras for Computer Vision : _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 páginas 109 ilustraciones, 106 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aAdvances in Computer Vision and Pattern Recognition, _x2191-6586
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aGall, Juergen. _eeditor. _9315787
700	1		_aGrabner, Helmut. _eeditor. _9315788
700	1		_aRen, Xiaofeng. _eeditor. _9315789
700	1		_aKonolige, Kurt. _eeditor. _9315790
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781447146391
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-4640-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c286868 _d286868