Cited by 69Advanced Vision and ImagingMedical Image Segmentation TechniquesVideo Surveillance and Tracking Methods

Optical flow estimation with uncertainties through dynamic MRFs

Glocker, Ben, Paragios, Nikos, Komodakis, Nikos, Tziritas, Georgios, Navab, Nassir·2008·2008 IEEE Conference on Computer Vision and Pattern Recognition

Abstract

In this paper, we propose a novel dynamic discrete framework to address image morphing with application to optical flow estimation. We reformulate the problem using a number of discrete displacements, and therefore the estimation of the morphing parameters becomes a tractable matching criteria independent combinatorial problem which is solved through the FastPD algorithm. In order to overcome the main limitation of discrete approaches (low dimensionality of the label space is unable to capture the continuous nature of the expected solution), we introduce a dynamic behavior in the model where the plausible discrete deformations (displacements) are varying in space (across the domain) and time (different states of the process - successive morphing states) according to the local uncertainty of the obtained solution.

Integrity signals

Registry

Crossref · 10.1109/cvpr.2008.4587562

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Dynamic MRF's performance degradation is only apparent on sequences with strong discontinuities (Grove, Urban, and Teddy).

    “quantitatively, the performance degradation is only apparent on the sequences with strong discontinuities ( Grove , Urban , and Teddy ).”

    From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Glocker et al. 2008

  • Only a few algorithms (e.g., DPOF, Fusion, and Dynamic MRF) perform well in this region.

    “Only a few algorithms (e.g., DPOF-Lei and Yang 2009, Fusion-Lempitsky et al. 2008, and Dynamic MRF-Glocker et al. 2008) perform well in this region.”

    From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Glocker et al. 2008

  • The flow fields for Dynamic MRF (Glocker et al. 2008) all appear to be over-smoothed.

    “The flow fields for Dynamic MRF (Glocker et al. 2008) all appear to be over-smoothed”

    From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Glocker et al. 2008

Cites

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1 of the 30 references couldn’t be matched to a known paper.

Cited by

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Authors
Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., & Navab, N.
Affiliations
  • Technical University of Munich (DE)
  • University of Crete (GR)
Venue
2008 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2008-06
Pages
1-8
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2163621828
Cited by
69
References
30
Citations by year
2022: 3 · 2021: 2 · 2020: 4 · 2019: 3 · 2018: 1 · 2017: 3 · 2016: 4 · 2015: 5 · 2014: 7 · 2013: 9 · 2012: 4
FWCI
6.14
Type
conference paper
Language
en
Open access
closed
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
  • HAL (submittedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Keywords
optical flow, image morphing, Markov random fields, combinatorial optimization, uncertainty modeling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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