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rdf:type
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Description
| - 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)
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Title
| - Approximate Models for Fast and Accurate Epipolar Geometry Estimation
- Approximate Models for Fast and Accurate Epipolar Geometry Estimation (en)
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skos:prefLabel
| - Approximate Models for Fast and Accurate Epipolar Geometry Estimation
- Approximate Models for Fast and Accurate Epipolar Geometry Estimation (en)
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skos:notation
| - RIV/68407700:21230/13:00212575!RIV15-MSM-21230___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
| - P(7E13016), P(GAP103/12/2310), P(LL1303)
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/13:00212575
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Epipolar Geometry; RANSAC (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - 2013 28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...vavai/riv/projekt
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Matas, Jiří
- Chum, Ondřej
- Pritts, James Brandon
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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issn
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number of pages
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http://bibframe.org/vocab/doi
| - 10.1109/IVCNZ.2013.6727000
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http://purl.org/ne...btex#hasPublisher
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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