Multi-frame Motion Segmentation by Combining Two-Frame Results

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-frame Motion Segmentation by Combining Two-Frame Results

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multi-frame Motion Segmentation by Combining Two-Frame Results

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Inteligentní strojové vnímání</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

International Journal of Computer Vision

ISSN

0920-5691

e-ISSN

1573-1405

Svazek periodika

130

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

33

Strana od-do

696-728

Kód UT WoS článku

000750293700001

EID výsledku v databázi Scopus

2-s2.0-85123958003