On the Usage of the Trifocal Tensor in Motion Segmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00347767" target="_blank" >RIV/68407700:21730/20:00347767 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-58565-5_31" target="_blank" >https://doi.org/10.1007/978-3-030-58565-5_31</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-58565-5_31" target="_blank" >10.1007/978-3-030-58565-5_31</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Usage of the Trifocal Tensor in Motion Segmentation

Popis výsledku v původním jazyce

Motion segmentation, i.e., the problem of clustering data in multiple images based on different 3D motions, is an important task for reconstructing and understanding dynamic scenes. In this paper we address motion segmentation in multiple images by combining partial results coming from triplets of images, which are obtained by fitting a number of trifocal tensors to correspondences. We exploit the fact that the trifocal tensor is a stronger model than the fundamental matrix, as it provides fewer but more reliable matches over three images than fundamental matrices provide over the two. We also consider an alternative solution which merges partial results coming from both triplets and pairs of images, showing the strength of three-frame segmentation in a combination with two-frame segmentation. Our real experiments on standard as well as new datasets demonstrate the superior accuracy of the proposed approaches when compared to previous techniques.

Název v anglickém jazyce

On the Usage of the Trifocal Tensor in Motion Segmentation

Popis výsledku anglicky

Motion segmentation, i.e., the problem of clustering data in multiple images based on different 3D motions, is an important task for reconstructing and understanding dynamic scenes. In this paper we address motion segmentation in multiple images by combining partial results coming from triplets of images, which are obtained by fitting a number of trifocal tensors to correspondences. We exploit the fact that the trifocal tensor is a stronger model than the fundamental matrix, as it provides fewer but more reliable matches over three images than fundamental matrices provide over the two. We also consider an alternative solution which merges partial results coming from both triplets and pairs of images, showing the strength of three-frame segmentation in a combination with two-frame segmentation. Our real experiments on standard as well as new datasets demonstrate the superior accuracy of the proposed approaches when compared to previous techniques.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computer Vision - ECCV 2020, Part XX

ISBN

978-3-030-58564-8

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

17

Strana od-do

514-530

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Glasgow

Datum konání akce

23. 8. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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