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Optical flow and trajectory estimati...

Gibson, Joel.

Optical flow and trajectory estimation methods

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optical flow and trajectory estimation methodsby Joel Gibson, Oge Marques.
Author:	Gibson, Joel.
other author:	Marques, Oge.
Published:	Cham :Springer International Publishing :2016.
Description:	x, 49 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Optical measurements.
Online resource:	http://dx.doi.org/10.1007/978-3-319-44941-8
ISBN:	9783319449418$q(electronic bk.)

Optical flow and trajectory estimation methods
Gibson, Joel.

Optical flow and trajectory estimation methods[electronic resource] /by Joel Gibson, Oge Marques. - Cham :Springer International Publishing :2016. - x, 49 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Optical Flow Fundamentals -- Optical Flow and Trajectory Methods in Context -- Sparse Regularization of TV-L Optical Flow -- Robust Low Rank Trajectories.

This 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.

ISBN: 9783319449418$q(electronic bk.)

Standard No.: 10.1007/978-3-319-44941-8doiSubjects--Topical Terms:

207767
Optical measurements.

LC Class. No.: QC367

Dewey Class. No.: 681.25

Optical flow and trajectory estimation methods
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245 1 0 $a Optical flow and trajectory estimation methods $h [electronic resource] / $c by Joel Gibson, Oge Marques.
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300 $a x, 49 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
505 0 $a Optical Flow Fundamentals -- Optical Flow and Trajectory Methods in Context -- Sparse Regularization of TV-L Optical Flow -- Robust Low Rank Trajectories.
520 $a This 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.
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650 1 4 $a Computer Science. $3 212513
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
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