Robust Subspace Estimation Using Low-Rank Optimization (Record no. 54703)
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fixed length control field | 03002nam a22004815i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-319-04184-1 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20200421111656.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 140324s2014 gw | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783319041841 |
-- | 978-3-319-04184-1 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 006.6 |
100 1# - AUTHOR NAME | |
Author | Oreifej, Omar. |
245 10 - TITLE STATEMENT | |
Title | Robust Subspace Estimation Using Low-Rank Optimization |
Sub Title | Theory and Applications / |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | VI, 114 p. 41 illus., 39 illus. in color. |
490 1# - SERIES STATEMENT | |
Series statement | The International Series in Video Computing, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Introduction -- 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 ## - SUMMARY, ETC. | |
Summary, etc | Various 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. |
700 1# - AUTHOR 2 | |
Author 2 | Shah, Mubarak. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-04184-1 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
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-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2014. |
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-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
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-- | text file |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer science. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer graphics. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Science. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 1571-5205 ; |
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-- | ZDB-2-SCS |
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