Robust Subspace Estimation Using Low-Rank Optimization Theory and Applications /

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 sig...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Oreifej, Omar (Autor), Shah, Mubarak (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2014.
Edición:1st ed. 2014.
Colección:The International Series in Video Computing ; 12
Temas:
Image processing-Digital techniques.
Computer vision.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Robust Subspace Estimation Using Low-Rank Optimization  |h [electronic resource] :  |b Theory and Applications /  |c by Omar Oreifej, Mubarak Shah. 
250 |a 1st ed. 2014. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2014. 
300 |a VI, 114 p. 41 illus., 39 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
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490 1 |a The International Series in Video Computing ;  |v 12 
505 0 |a 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 |a 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. 
650 0 |a Image processing-Digital techniques. 
650 0 |a Computer vision. 
650 1 4 |a Computer Imaging, Vision, Pattern Recognition and Graphics. 
700 1 |a Shah, Mubarak.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319041858 
776 0 8 |i Printed edition:  |z 9783319041834 
776 0 8 |i Printed edition:  |z 9783319352480 
830 0 |a The International Series in Video Computing ;  |v 12 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-319-04184-1  |z Texto Completo 
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950 |a Computer Science (SpringerNature-11645) 
950 |a Computer Science (R0) (SpringerNature-43710) 

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