Multi-frame Motion Segmentation by Combining Two-Frame Results
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00355484" target="_blank" >RIV/68407700:21730/22:00355484 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s11263-021-01544-x" target="_blank" >https://doi.org/10.1007/s11263-021-01544-x</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11263-021-01544-x" target="_blank" >10.1007/s11263-021-01544-x</a>
Alternative languages
Result language
angličtina
Original language name
Multi-frame Motion Segmentation by Combining Two-Frame Results
Original language description
In this paper we consider the motion segmentation problem on sparse and unstructured datasets involving rigid motions, motivated by multibody structure from motion. In particular, we assume only two-frame correspondences as input without prior knowledge about trajectories. Inspired by the success of synchronization methods, we address this problem by introducing a two-stage approach: first, motion segmentation is addressed on image pairs independently; then, two-frame results are combined in a robust way to compute the final multi-frame segmentation. Our synthetic and real experiments demonstrate that the proposed approach is very effective in reducing the errors among two-frame results and it can cope with a large amount of mismatches. Moreover, our method can be profitably used to build a multibody structure from motion pipeline.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Computer Vision
ISSN
0920-5691
e-ISSN
1573-1405
Volume of the periodical
130
Issue of the periodical within the volume
3
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
33
Pages from-to
696-728
UT code for WoS article
000750293700001
EID of the result in the Scopus database
2-s2.0-85123958003