Low-Rank and Sparse Modeling for Visual Analysis / edited by Yun Fu

By:

Fu, Yun [aut]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink Bücher | Springer eBook Collection Computer SciencePublisher: Cham : Springer, 2014Description: Online-Ressource (VII, 236 p. 66 illus., 51 illus. in color, online resource)ISBN:

9783319120003

Subject(s):

Additional physical formats: 9783319119991 | Erscheint auch als: Low-rank and sparse modeling for visual analysis. Druck-Ausgabe. Cham : Springer, 2014. VII, 236 S.DDC classification:

006.6
006.37

MSC: MSC: *68-06 | 68U10 | 68T45 | 00B15LOC classification:

TA1637-1638 TA1637-1638
TA1637-1638

DOI: DOI: 10.1007/978-3-319-12000-3Online resources:

Summary: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. Included in the book are chapters covering multiple emerging topics in this new field. The text links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. This book contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications. · Covers the most state-of-the-art topics of sparse and low-rank modeling · Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis · Contributions from top experts voicing their unique perspectives included throughoutPPN: PPN: 1659487420Package identifier: Produktsigel: ZDB-2-SCS

Holdings ( 0 )

No physical items for this record