000 | 03002nam a22004815i 4500 | ||
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001 | 978-3-319-04184-1 | ||
003 | DE-He213 | ||
005 | 20200421111656.0 | ||
007 | cr nn 008mamaa | ||
008 | 140324s2014 gw | s |||| 0|eng d | ||
020 |
_a9783319041841 _9978-3-319-04184-1 |
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024 | 7 |
_a10.1007/978-3-319-04184-1 _2doi |
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050 | 4 | _aT385 | |
050 | 4 | _aTA1637-1638 | |
050 | 4 | _aTK7882.P3 | |
072 | 7 |
_aUYQV _2bicssc |
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072 | 7 |
_aCOM016000 _2bisacsh |
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082 | 0 | 4 |
_a006.6 _223 |
100 | 1 |
_aOreifej, Omar. _eauthor. |
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245 | 1 | 0 |
_aRobust Subspace Estimation Using Low-Rank Optimization _h[electronic resource] : _bTheory and Applications / _cby Omar Oreifej, Mubarak Shah. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014. |
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300 |
_aVI, 114 p. 41 illus., 39 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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490 | 1 |
_aThe International Series in Video Computing, _x1571-5205 ; _v12 |
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505 | 0 | _aIntroduction -- Background and Literature Review -- Seeing Through Water: Underwater Scene Reconstruction -- Simultaneous Turbulence Mitigation and Moving Object Detection -- Action Recognition by Motion Trajectory Decomposition -- Complex Event Recognition Using Constrained Rank Optimization -- Concluding Remarks -- Extended Derivations for Chapter 4. | |
520 | _aVarious fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aComputer graphics. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
700 | 1 |
_aShah, Mubarak. _eauthor. |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319041834 |
830 | 0 |
_aThe International Series in Video Computing, _x1571-5205 ; _v12 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-04184-1 |
912 | _aZDB-2-SCS | ||
942 | _cEBK | ||
999 |
_c54703 _d54703 |