Bootstrap Optical Flow Confidence and Uncertainty Measure
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Bootstrap Optical Flow Confidence and Uncertainty Measure
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
—
Result continuities
Project
—
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer Vision and Image Understanding
ISSN
1077-3142
e-ISSN
—
Volume of the periodical
115
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
1449-1462
UT code for WoS article
000294395900008
EID of the result in the Scopus database
—