High Accuracy Homography Computation without Iterations

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Abstract

We present highly accurate least-squares (LS) alternatives to the theoretically optimal maximum likelihood (ML) estimator for homographies between two images. Unlike ML, our estimators are non-iterative and yield solutions even in the presence of large noise. By rigorous error analysis, we derive a "hyperaccurate" estimator which is unbiased up to second order noise terms. Then, we introduce a computational simplification, which we call "Taubin approximation", without incurring a loss in accuracy. We experimentally demonstrate that our estimators have accuracy surpassing the traditional LS estimator and comparable to the ML estimator.

Journal

  • Memoirs of the Faculty of Engineering, Okayama University

    Memoirs of the Faculty of Engineering, Okayama University (44), 50-59, 2010-01

    Faculty of Engineering, Okayama University

Codes

  • NII Article ID (NAID)
    120002308986
  • NII NACSIS-CAT ID (NCID)
    AA12014085
  • Text Lang
    ENG
  • Article Type
    departmental bulletin paper
  • ISSN
    1349-6115
  • Data Source
    IR 
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