000			03907nam a22006495i 4500
001			978-3-030-96530-3
003			DE-He213
005			20240207153720.0
007			cr nn 008mamaa
008			230315s2023 sz | s |||| 0|eng d
020			_a9783030965303 _9978-3-030-96530-3
050		4	_aTA1501-1820
050		4	_aTA1634
072		7	_aUYT _2bicssc
072		7	_aCOM016000 _2bisacsh
072		7	_aUYT _2thema
082	0	4	_a006 _223
100	1		_aZhu, Song-Chun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aComputer Vision _h[electronic resource] : _bStatistical Models for Marr's Paradigm / _cby Song-Chun Zhu, Ying Nian Wu.
250			_a1st ed. 2023.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023.
300			_aXIV, 357 p. 192 illus., 109 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
500			_aAcceso multiusuario
505	0		_aPreface -- About the Authors -- 1 Introduction -- 2 Statistics of Natural Images -- 3 Textures -- 4 Textons -- 5 Gestalt Laws and Perceptual Organizations -- 6 Primal Sketch: Integrating Textures and Textons -- 7 2.1D Sketch and Layered Representation -- 8 2.5D Sketch and Depth Maps -- 9 Learning about information Projection -- 10 Informing Scaling and Regimes of Models -- 11 Deep Images and Models -- 12 A Tale of Three Families: Discriminative, Generative and Descriptive Models -- Bibliography.
520			_aAs the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The authors hope to provide foundation and, perhaps more importantly, further inspiration for continued research in vision. This book covers David Marr's paradigm and various underlying statistical models for vision. The mathematical framework herein integrates three regimes of models (low-, mid-, and high-entropy regimes) and provides foundation for research in visual coding, recognition, and cognition. Concepts are first explained for understanding and then supported by findings in psychology and neuroscience, after which they are established by statistical models and associated learning and inference algorithms. A reader will gain a unified, cross-disciplinary view of research in vision and will accrue knowledge spanning from psychology to neuroscience to statistics.
541			_fUABC ; _cPerpetuidad
650		0	_aImage processing _xDigital techniques.
650		0	_aComputer vision.
650		0	_aInformation visualization.
650		0	_aComputer science.
650		0	_aComputer science _xMathematics.
650		0	_aMathematical statistics.
650		0	_aNeural networks (Computer science) .
650	1	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aData and Information Visualization.
650	2	4	_aTheory of Computation.
650	2	4	_aProbability and Statistics in Computer Science.
650	2	4	_aComputer Science.
650	2	4	_aMathematical Models of Cognitive Processes and Neural Networks.
700	1		_aWu, Ying Nian. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030965297
776	0	8	_iPrinted edition: _z9783030965310
776	0	8	_iPrinted edition: _z9783030965327
856	4	0	_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-030-96530-3
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cLIBRO_ELEC
999			_c262855 _d262854