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Optical Flow and Trajectory Estimation Methods
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. Beginn...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Colección: | SpringerBriefs in Computer Science,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Gibson, Joel. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Optical Flow and Trajectory Estimation Methods |h [electronic resource] / |c by Joel Gibson, Oge Marques. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2016. | |
| 300 | |a X, 49 p. 6 illus. |b online resource. | ||
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| 490 | 1 | |a SpringerBriefs in Computer Science, |x 2191-5776 | |
| 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. | ||
| 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 Marques, Oge. |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 9783319449401 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319449425 |
| 830 | 0 | |a SpringerBriefs in Computer Science, |x 2191-5776 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-44941-8 |z Texto Completo |
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| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||