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  • This paper investigates the plausibility of using approximate models for hypothesis generation in a RANSAC framework to accurately and reliably estimate the fundamental matrix. Two novel fundamental matrix estimators are introduced that sample two correspondences to generate affine-fundamental matrices for RANSAC hypotheses. A new RANSAC framework is presented that uses local optimization to estimate the fundamental matrix from the consensus correspondence sets of verified hy- potheses, which are approximate models. The proposed estimators are shown to perform better than other approximate models that have previously been used in the literature for fundamental matrix estimation in a rigorous evaluation. In addition the proposed estimators are over 30 times faster, in terms of models verified, than the 7-point method, and offer comparable accuracy and repeatability on a large subset of the test set.
  • This paper investigates the plausibility of using approximate models for hypothesis generation in a RANSAC framework to accurately and reliably estimate the fundamental matrix. Two novel fundamental matrix estimators are introduced that sample two correspondences to generate affine-fundamental matrices for RANSAC hypotheses. A new RANSAC framework is presented that uses local optimization to estimate the fundamental matrix from the consensus correspondence sets of verified hy- potheses, which are approximate models. The proposed estimators are shown to perform better than other approximate models that have previously been used in the literature for fundamental matrix estimation in a rigorous evaluation. In addition the proposed estimators are over 30 times faster, in terms of models verified, than the 7-point method, and offer comparable accuracy and repeatability on a large subset of the test set. (en)
Title
  • Approximate Models for Fast and Accurate Epipolar Geometry Estimation
  • Approximate Models for Fast and Accurate Epipolar Geometry Estimation (en)
skos:prefLabel
  • Approximate Models for Fast and Accurate Epipolar Geometry Estimation
  • Approximate Models for Fast and Accurate Epipolar Geometry Estimation (en)
skos:notation
  • RIV/68407700:21230/13:00212575!RIV15-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(7E13016), P(GAP103/12/2310), P(LL1303)
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
  • 61892
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/13:00212575
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Epipolar Geometry; RANSAC (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [24DDFEA2D734]
http://linked.open...v/mistoKonaniAkce
  • Wellington
http://linked.open...i/riv/mistoVydani
  • Piscataway
http://linked.open...i/riv/nazevZdroje
  • 2013 28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Matas, Jiří
  • Chum, Ondřej
  • Pritts, James Brandon
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 2151-2191
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/IVCNZ.2013.6727000
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 978-1-4799-0882-0
http://localhost/t...ganizacniJednotka
  • 21230
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