000 | 03769nam a22006255i 4500 | ||
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001 | 978-3-030-96530-3 | ||
003 | DE-He213 | ||
005 | 20240423130120.0 | ||
007 | cr nn 008mamaa | ||
008 | 230315s2023 sz | s |||| 0|eng d | ||
020 |
_a9783030965303 _9978-3-030-96530-3 |
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024 | 7 |
_a10.1007/978-3-030-96530-3 _2doi |
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050 | 4 | _aTA1501-1820 | |
050 | 4 | _aTA1634 | |
072 | 7 |
_aUYT _2bicssc |
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072 | 7 |
_aCOM016000 _2bisacsh |
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072 | 7 |
_aUYT _2thema |
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082 | 0 | 4 |
_a006 _223 |
100 | 1 |
_aZhu, Song-Chun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIV, 357 p. 192 illus., 109 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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. | ||
650 | 0 |
_aImage processing _xDigital techniques. |
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650 | 0 | _aComputer vision. | |
650 | 0 | _aInformation visualization. | |
650 | 0 | _aComputer science. | |
650 | 0 |
_aComputer science _xMathematics. |
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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 | _uhttps://doi.org/10.1007/978-3-030-96530-3 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c185214 _d185214 |