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  • 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.
  • 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. (en)
Title
  • Bootstrap Optical Flow Confidence and Uncertainty Measure
  • Bootstrap Optical Flow Confidence and Uncertainty Measure (en)
skos:prefLabel
  • Bootstrap Optical Flow Confidence and Uncertainty Measure
  • Bootstrap Optical Flow Confidence and Uncertainty Measure (en)
skos:notation
  • RIV/68407700:21230/11:00181658!RIV12-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770012)
http://linked.open...iv/cisloPeriodika
  • 10
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 188626
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/11:00181658
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • optical flow; bootstrap; confidence measure; motion estimation; uncertainty estimatio (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [CB8C89A7E4F6]
http://linked.open...i/riv/nazevZdroje
  • Computer Vision and Image Understanding
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 115
http://linked.open...iv/tvurceVysledku
  • Kybic, Jan
  • Nieuwenhuis, C.
http://linked.open...ain/vavai/riv/wos
  • 000294395900008
http://linked.open...n/vavai/riv/zamer
issn
  • 1077-3142
number of pages
http://bibframe.org/vocab/doi
  • 10.1016/j.cviu.2011.06.008
http://localhost/t...ganizacniJednotka
  • 21230
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