cam a22 5 4500 317160087 CHVBK 20201009192933.0 m d cr |n |||||||| 140625s2014 gw |||| s 0 0|eng d 978-3-319-04184-1 978-3-319-04183-4 (print) 10.1007/978-3-319-04184-1 doi (SERSOL)ssj0001186560 (NEBIS)010157810 (IDSSG)000655415 (WaSeSS)ssj0001186560 WaSeSS T385 TA1637-1638 TK7882.P3 UYQV bicssc COM016000 bisacsh 006.6 23 CH0016E967F53318B3C01C1257F0700701C7A iCapture Oreifej Omar author Robust Subspace Estimation Using Low-Rank Optimization [Elektronische Daten] Theory and Applications by Omar Oreifej, Mubarak Shah Cham Springer International Publishing Imprint: Springer 2014 1 online resource (VI, 114 p. 41 illus., 39 illus. in color.) online resource text file PDF rda The International Series in Video Computing 1571-5205 12 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. Lizenzbedingungen können den Zugang einschränken. License restrictions may limit access. Recovering the low-rank structure of a linear subspace using a small set of corrupted examples has recently been made feasible through substantial advances in the area of matrix completion and nuclear-norm minimization. Such low-rank structures appear in certain conditions heavily in computer vision, for instance, in the frames of a video, the camera motion, and a picture of a building façade. In this book, we discuss several formulations and extensions of low-rank optimization, and demonstrate how recovering the underlying basis and detecting the corresponding outliers allow us to solve fundamental computer vision problems, including video denoising, background subtraction, action detection, and complex event recognition. COMPUTERVISION (KÜNSTLICHE INTELLIGENZ) ger (ETHUDK)000040001 ethudk MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ) ger (ETHUDK)000038192 ethudk Computer science Computer vision Online-Ressource gnd-carrier u COMPUTERVISION (KÜNSTLICHE INTELLIGENZ) ger 004*08*02*10 nebis E1 u MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ) ger 004*08*02*07 nebis E1 u VISION ARTIFICIELLE + COMPRÉHENSION D'IMAGES (INTELLIGENCE ARTIFICIELLE) fre 004*08*02*10 nebis E1 u COMPUTER VISION + SCENE UNDERSTANDING (ARTIFICIAL INTELLIGENCE) eng 004*08*02*10 nebis E1 u MACHINE LEARNING (ARTIFICIAL INTELLIGENCE) eng 004*08*02*07 nebis E1 u APPRENTISSAGE AUTOMATIQUE (INTELLIGENCE ARTIFICIELLE) fre 004*08*02*07 nebis E1 Shah Mubarak author SpringerLink (Online service) Printed edition 9783319041834 https://link.springer.com/10.1007/978-3-319-04184-1 Uni Basel: Volltext BK020053 XK020053 XK020000 Springer Computer Science eBooks 2014 English/International E-Books von 360MarcUpdates EText nebis ED Computer Science (Springer-11645) nebis ER E01-Springer201406 nebis ER ebphzh eb-Springer idsuzh U2 Update201606 idsuzh U2 E65-Springer201606 nebis ER noalma ids I 098 E01-20151111 UPHZHEBO UPHZH-201606 NEBIS UPHZH UPHZH ULONL IDSSG HSG HSG ONL HONL000655415 IDSBB A145 A145 145VT IDSBB 100 1- Oreifej Omar author IDSBB 490 1- The International Series in Video Computing 1571-5205 12 IDSBB 700 1- Shah Mubarak author IDSBB 710 2- SpringerLink (Online service) IDSBB 776 08 Printed edition 9783319041834 IDSBB 856 40 https://link.springer.com/10.1007/978-3-319-04184-1 Uni Basel: Volltext NEBIS 490 -- The International Series in Video Computing NEBIS 700 1- Oreifej Omar NEBIS 700 1- Shah Mubarak NEBIS 856 -- http://sfx.ethz.ch/sfx_locater?sid=ALEPH:EBI01&genre=book&isbn=9783319041834 Online via SFX Volltext IDSSG 490 0- The International Series in Video Computing 12 IDSSG 700 1- Oreifej Omar IDSSG 700 1- Shah Mubarak IDSSG 856 4- https://doi.org/10.1007/978-3-319-04184-1 Full text for the University of St. Gallen