Bootstrap Optical Flow Confidence and Uncertainty Measure

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00181658" target="_blank" >RIV/68407700:21230/11:00181658 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.cviu.2011.06.008" target="_blank" >http://dx.doi.org/10.1016/j.cviu.2011.06.008</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cviu.2011.06.008" target="_blank" >10.1016/j.cviu.2011.06.008</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bootstrap Optical Flow Confidence and Uncertainty Measure

Popis výsledku v původním jazyce

We address the problem of estimating the uncertainty of optical flow algorithm results. Our method estimates the error magnitude at all points in the image. It can be used as a confidence measure. It is based on bootstrap resampling, which is a computational statistical inference technique based on repeating the optical flow calculation several times for different randomly chosen subsets of pixel contributions. As few as 10 repetitions are enough to obtain useful estimates of geometrical and angular errors. We use the combined local global optical flow method (CLG) which generalizes both Lucas-Kanade and Horn-Schunck type methods. However, the bootstrap method is very general and can be applied to almost any optical flow algorithm that can be formulated as a minimization problem. We show experimentally on synthetic as well as real video sequences with known ground truth that the bootstrap method performs better than all other confidence measures tested.

Název v anglickém jazyce

Bootstrap Optical Flow Confidence and Uncertainty Measure

Popis výsledku anglicky

We address the problem of estimating the uncertainty of optical flow algorithm results. Our method estimates the error magnitude at all points in the image. It can be used as a confidence measure. It is based on bootstrap resampling, which is a computational statistical inference technique based on repeating the optical flow calculation several times for different randomly chosen subsets of pixel contributions. As few as 10 repetitions are enough to obtain useful estimates of geometrical and angular errors. We use the combined local global optical flow method (CLG) which generalizes both Lucas-Kanade and Horn-Schunck type methods. However, the bootstrap method is very general and can be applied to almost any optical flow algorithm that can be formulated as a minimization problem. We show experimentally on synthetic as well as real video sequences with known ground truth that the bootstrap method performs better than all other confidence measures tested.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

Computer Vision and Image Understanding

ISSN

1077-3142

e-ISSN

—

Svazek periodika

115

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1449-1462

Kód UT WoS článku

000294395900008

EID výsledku v databázi Scopus

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