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010   $a3-319-44941-9
024 7 $a10.1007/978-3-319-44941-8
035   $a(CKB)3710000000843008
035   $a(EBL)4662709
035   $a(DE-He213)978-3-319-44941-8
035   $a(MiAaPQ)EBC4662709
035   $z(PPN)258861401
035   $a(PPN)195512162
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100   $a20160901d2016      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aOptical Flow and Trajectory Estimation Methods /$fby Joel Gibson, Oge Marques
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (57 p.)
225 1 $aSpringerBriefs in Computer Science,$x2191-5768
300   $aDescription based upon print version of record.
311   $a3-319-44940-0 
327   $aOptical Flow Fundamentals -- Optical Flow and Trajectory Methods in Context -- Sparse Regularization of TV-L Optical Flow -- Robust Low Rank Trajectories.
330   $aThis brief focuses on two main problems in the domain of optical flow and trajectory estimation: (i) The problem of finding convex optimization methods to apply sparsity to optical flow; and (ii) The problem of how to extend sparsity to improve trajectories in a computationally tractable way. Beginning with a review of optical flow fundamentals, it discusses the commonly used flow estimation strategies and the advantages or shortcomings of each. The brief also introduces the concepts associated with sparsity including dictionaries and low rank matrices. Next, it provides context for optical flow and trajectory methods including algorithms, data sets, and performance measurement. The second half of the brief covers sparse regularization of total variation optical flow and robust low rank trajectories. The authors describe a new approach that uses partially-overlapping patches to accelerate the calculation and is implemented in a coarse-to-fine strategy. Experimental results show that combining total variation and a sparse constraint from a learned dictionary is more effective than employing total variation alone. The brief is targeted at researchers and practitioners in the fields of engineering and computer science. It caters particularly to new researchers looking for cutting edge topics in optical flow as well as veterans of optical flow wishing to learn of the latest advances in multi-frame methods.
410  0$aSpringerBriefs in Computer Science,$x2191-5768
606   $aOptical data processing
606   $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005
615  0$aOptical data processing.
615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics.
676   $a004
700   $aGibson$b Joel$4aut$4http://id.loc.gov/vocabulary/relators/aut$0871283
702   $aMarques$b Oge$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910254980903321
996   $aOptical Flow and Trajectory Estimation Methods$91944978
997   $aUNINA