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Christian Lipski, Christian Linz, Thomas Neumann, Markus Wacker, and Marcus Magnor, High Resolution Image Correspondences for Video Post-Production. JVRB - Journal of Virtual Reality and Broadcasting, 9(2012), no. 8. (urn:nbn:de:0009-6-35543)

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%0 Journal Article
%T High Resolution Image Correspondences for Video Post-Production
%A Lipski, Christian
%A Linz, Christian
%A Neumann, Thomas
%A Wacker, Markus
%A Magnor, Marcus
%J JVRB - Journal of Virtual Reality and Broadcasting
%D 2012
%V 9(2012)
%N 8
%@ 1860-2037
%F lipski2012
%X We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization.While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.
%L 004
%K Belief Propagation
%K Dense Image Correspondences
%K Depth Reconstruction
%K Optical Flow
%K Video Post-Production
%R 10.20385/1860-2037/9.2012.8
%U http://nbn-resolving.de/urn:nbn:de:0009-6-35543
%U http://dx.doi.org/10.20385/1860-2037/9.2012.8
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Bibtex

@Article{lipski2012,
  author = 	"Lipski, Christian
		and Linz, Christian
		and Neumann, Thomas
		and Wacker, Markus
		and Magnor, Marcus",
  title = 	"High Resolution Image Correspondences for Video Post-Production",
  journal = 	"JVRB - Journal of Virtual Reality and Broadcasting",
  year = 	"2012",
  volume = 	"9(2012)",
  number = 	"8",
  keywords = 	"Belief Propagation; Dense Image Correspondences; Depth Reconstruction; Optical Flow; Video Post-Production",
  abstract = 	"We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization.While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.",
  issn = 	"1860-2037",
  doi = 	"10.20385/1860-2037/9.2012.8",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-6-35543"
}
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RIS

TY  - JOUR
AU  - Lipski, Christian
AU  - Linz, Christian
AU  - Neumann, Thomas
AU  - Wacker, Markus
AU  - Magnor, Marcus
PY  - 2012
DA  - 2012//
TI  - High Resolution Image Correspondences for Video Post-Production
JO  - JVRB - Journal of Virtual Reality and Broadcasting
VL  - 9(2012)
IS  - 8
KW  - Belief Propagation
KW  - Dense Image Correspondences
KW  - Depth Reconstruction
KW  - Optical Flow
KW  - Video Post-Production
AB  - We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization.While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.
SN  - 1860-2037
UR  - http://nbn-resolving.de/urn:nbn:de:0009-6-35543
DO  - 10.20385/1860-2037/9.2012.8
ID  - lipski2012
ER  -
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Wordbib

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<b:Comments>We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization.While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.</b:Comments>
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ISI

PT Journal
AU Lipski, C
   Linz, C
   Neumann, T
   Wacker, M
   Magnor, M
TI High Resolution Image Correspondences for Video Post-Production
SO JVRB - Journal of Virtual Reality and Broadcasting
PY 2012
VL 9(2012)
IS 8
DI 10.20385/1860-2037/9.2012.8
DE Belief Propagation; Dense Image Correspondences; Depth Reconstruction; Optical Flow; Video Post-Production
AB We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization.While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.
ER
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Mods

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  <name type="personal">
    <namePart type="family">Lipski</namePart>
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    <namePart type="given">Christian</namePart>
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  <abstract>We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization.

While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.</abstract>
  <subject>
    <topic>Belief Propagation</topic>
    <topic>Dense Image Correspondences</topic>
    <topic>Depth Reconstruction</topic>
    <topic>Optical Flow</topic>
    <topic>Video Post-Production</topic>
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Full Metadata

Bibliographic Citation JVRB, 9(2012), no. 8.
Title

High Resolution Image Correspondences for Video Post-Production (eng)
Author Christian Lipski, Christian Linz, Thomas Neumann, Markus Wacker, Marcus Magnor
Language eng
Abstract We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization. While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.
Subject Belief Propagation, Dense Image Correspondences, Depth Reconstruction, Optical Flow, Video Post-Production
Classified Subjects
swd
  • 4476646-4, Videoproduktion
  • 4536854-5, Videobearbeitung
DDC 004
Rights DPPL
URN: urn:nbn:de:0009-6-35543
DOI https://doi.org/10.20385/1860-2037/9.2012.8