Iterative extensions of the sturm/triggs algorithm: Convergence and nonconvergence

John Oliensis*, Richard Hartley

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Citations (Scopus)

    Abstract

    We show that SIESTA, the simplest iterative extension of the Sturm/Triggs algorithm, descends an error function. However, we prove that SIESTA does not converge to usable results. The iterative extension of Mahamud et al. has similar problems, and experiments with "balanced" iterations show that they can fail to converge. We present CIESTA, an algorithm which avoids these problems. It is identical to SIESTA except for one extra, simple stage of computation. We prove that CIESTA descends an error and approaches fixed points. Under weak assumptions, it converges. The CIESTA error can be minimized using a standard descent method such as Gauss-Newton, combining quadratic convergence with the advantage of minimizing in the projective depths.

    Original languageEnglish
    Title of host publicationComputer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings
    Pages214-227
    Number of pages14
    DOIs
    Publication statusPublished - 2006
    Event9th European Conference on Computer Vision, ECCV 2006 - Graz, Austria
    Duration: 7 May 200613 May 2006

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3954 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference9th European Conference on Computer Vision, ECCV 2006
    Country/TerritoryAustria
    CityGraz
    Period7/05/0613/05/06

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