Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Kai Ruhl"'
Publikováno v:
Image and Vision Computing. 30:641-654
Many applications in computer vision and computer graphics require dense correspondences between images of multi-view video streams. Most state-of-the-art algorithms estimate correspondences by considering pairs of images. However, in multi-view vide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0779be5a19162ab25b8e864d2d0ed969
https://doi.org/10.1016/j.imavis.2012.06.011
https://doi.org/10.1016/j.imavis.2012.06.011
Publikováno v:
ACM Multimedia
High-quality stereo and optical flow maps are essential for a multitude of tasks in visual media production, e.g. virtual camera navigation, disparity adaptation or scene editing. Rather than estimating stereo and optical flow separately, scene flow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e48db143416cb02a6ef19fd95e2c607
https://doi.org/10.1145/2733373.2806251
https://doi.org/10.1145/2733373.2806251
Publikováno v:
CVMP
In the post production of a stereoscopic visual media production, high-quality depth or disparity maps are essential for a number of workflow components, from initial layer separation, over editing transfers, to the final depth compositing. With auto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bd378d6b427bea4bfecf4ece776194b
https://doi.org/10.1145/2534008.2534009
https://doi.org/10.1145/2534008.2534009
Autor:
Rahul Nair, Kai Ruhl, Stephan Meister, Henrik Schxe4fer, Christoph S. Garbe, Martin Eisemann, Marcus Magnor, Daniel Kondermann
Publikováno v:
A Survey on Time-of-Flight Stereo Fusion.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=erc_________::adab26689c26db511dbd0a4a55689cb2
Autor:
Christoph S. Garbe, Daniel Kondermann, Henrik Schäfer, Marcus Magnor, Rahul Nair, Stephan Meister, Kai Ruhl, Frank Lenzen, Martin Eisemann
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642449635
Time-of-Flight and Depth Imaging
Time-of-Flight and Depth Imaging
Due to the demand for depth maps of higher quality than possible with a single depth imaging technique today, there has been an increasing interest in the combination of different depth sensors to produce a “super-camera” that is more than the su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::980d052af7b3642301f14a0f74bd6571
https://doi.org/10.1007/978-3-642-44964-2_6
https://doi.org/10.1007/978-3-642-44964-2_6
Publikováno v:
ACM Multimedia
High quality dense image correspondence estimation between two images is an essential pre-requisite for view interpolation in visual media production. Due to the ill-posed nature of the problem, automated estimation approaches are prone to erroneous
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97418f476a2d414114db7bb8913b9aec
https://doi.org/10.1145/2393347.2396400
https://doi.org/10.1145/2393347.2396400
Publikováno v:
Integrating Approximate Depth Data into Dense Image Correspondence Estimation.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3dd0329059982b8b0ac4abbaf49a0396
Publikováno v:
Improving Dense Image Correspondence Estimation with Interactive User Guidance.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=erc_________::6c8277fbafb4c4a1eb376cae2968401f
Publikováno v:
Journal of Image and Vision Computing, vol. 30.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=erc_________::ab174724025b6be3411e759fec583f6f
Publikováno v:
CVMP
High-quality dense image correspondence estimation between two images is an essential prerequisite for many tasks in visual media production, one prominent example being view interpolation. Due to the ill-posed nature of the correspondence estimation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f29c2c01f1f14de8967c22a090a152c3
https://doi.org/10.1145/2414688.2414692
https://doi.org/10.1145/2414688.2414692