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  • In this paper we propose methods for speeding up minimal solvers based on Gröbner bases and action matrix eigenvalue computations. Almost all existing Gröbner basis solvers spend most time in the eigenvalue computation. We present two methods which speed up this phase of Gröbner basis solvers: (1) a method based on a modified FGLM algorithm for transforming Gröbner bases which results in a single-variable polynomial followed by direct calculation of its roots using Sturm-sequences and, for larger problems, (2) fast calculation of the characteristic polynomial of an action matrix, again solved using Sturm-sequences. We enhanced the FGLM method by replacing time consuming polynomial division performed in standard FGLM algorithm with efficient matrix-vector multiplication and we show how this method is related to the characteristic polynomial method. Our approaches allow computing roots only in some feasible interval and in desired precision. Proposed methods can significantly speedup many existing solvers. We demonstrate them on three important minimal computer vision problems.
  • In this paper we propose methods for speeding up minimal solvers based on Gröbner bases and action matrix eigenvalue computations. Almost all existing Gröbner basis solvers spend most time in the eigenvalue computation. We present two methods which speed up this phase of Gröbner basis solvers: (1) a method based on a modified FGLM algorithm for transforming Gröbner bases which results in a single-variable polynomial followed by direct calculation of its roots using Sturm-sequences and, for larger problems, (2) fast calculation of the characteristic polynomial of an action matrix, again solved using Sturm-sequences. We enhanced the FGLM method by replacing time consuming polynomial division performed in standard FGLM algorithm with efficient matrix-vector multiplication and we show how this method is related to the characteristic polynomial method. Our approaches allow computing roots only in some feasible interval and in desired precision. Proposed methods can significantly speedup many existing solvers. We demonstrate them on three important minimal computer vision problems. (en)
Title
  • Making Minimal Solvers Fast
  • Making Minimal Solvers Fast (en)
skos:prefLabel
  • Making Minimal Solvers Fast
  • Making Minimal Solvers Fast (en)
skos:notation
  • RIV/68407700:21230/12:00200360!RIV13-MSM-21230___
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http://linked.open...avai/riv/aktivita
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  • P(7E09062), P(7E10046), S
http://linked.open...vai/riv/dodaniDat
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  • 147867
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/12:00200360
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Minimal problems; Gröbner basis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [712A1FA6D4D5]
http://linked.open...v/mistoKonaniAkce
  • Providence, Rhode Island
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • CVPR 2012: Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
http://linked.open...in/vavai/riv/obor
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Bujňák, Martin
  • Kúkelová, Zuzana
  • Pajdla, Tomáš
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000309166201083
http://linked.open.../riv/zahajeniAkce
issn
  • 1063-6919
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/CVPR.2012.6247853
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society Press
https://schema.org/isbn
  • 978-1-4673-1228-8
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  • 21230
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