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Improved confidence measures for variational optical flow
Citation Link: https://doi.org/10.15480/882.3606
Publikationstyp
Conference Paper
Date Issued
2015
Sprache
English
Institut
TORE-DOI
TORE-URI
Start Page
389
End Page
394
Citation
Proceedings of the 10th International Conference on Computer Vision Theory and Applications Vol. 1: 389-394 (2015)
Contribution to Conference
Publisher DOI
Publisher
SCITEPRESS
In the last decades variational optical flow algorithms have been intensively studied by the computer vision community. However, relatively few effort has been made to obtain robust confidence measures for the estimated flow field. As many applications do not require the whole flow field, it would be helpful to identify the parts of the field where the flow is most accurate. We propse a confidence measure based on the energy functional that is minimized during the optical flow calculation and analyze the performance of different data terms. For evaluation, 7 datasets of the Middlebury benchmark are used. The results show that the accuracy of the flow field can be improved by 53.3% if points are selected according to the proposed confidence measure. The suggested method leads to an improvement of 35.2% compared to classical confidence measures.
Subjects
Variational Optical Flow
Confidence Measure
Performance Evaluation
Structure-Texture Decomposition
DDC Class
004: Informatik
600: Technik
Publication version
publishedVersion
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51672.pdf
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229.39 KB
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