000 02980nam a22004695i 4500
001 978-3-319-04184-1
003 DE-He213
005 20170628034109.0
007 cr nn 008mamaa
008 140324s2014 gw | s |||| 0|eng d
020 _a9783319041841
_9978-3-319-04184-1
024 7 _a10.1007/978-3-319-04184-1
_2doi
050 4 _aT385
050 4 _aTA1637-1638
050 4 _aTK7882.P3
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
100 1 _aOreifej, Omar.
_eauthor.
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.
300 _aVI, 114 p. 41 illus., 39 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 _aThe International Series in Video Computing,
_x1571-5205 ;
_v12
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 vision.
650 1 4 _aComputer Science.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
700 1 _aShah, Mubarak.
_eauthor.
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
999 _c18275
_d18275