Unified Computation of Strict Maximum Likelihood for Geometric Fitting

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Abstract

A new numerical scheme is presented for computing strict maximum likelihood (ML) of geometricfitting problems having an implicit constraint. Our approach is orthogonal projection of observationsonto a parameterized surface defined by the constraint. Assuming a linearly separable nonlinear constraint, we show that a theoretically global solution can be obtained by iterative Sampson error minimization. Our approach is illustrated by ellipse fitting and fundamental matrix computation. Our method also encompasses optimal correction, computing, e.g., perpendiculars to an ellipse and triangulating stereo images. A detailed discussion is given to technical and practical issues about our approach.

Journal

  • Memoirs of the Faculty of Engineering, Okayama University

    Memoirs of the Faculty of Engineering, Okayama University (44), 13-23, 2010-01

    Faculty of Engineering, Okayama University

Codes

  • NII Article ID (NAID)
    120002309170
  • NII NACSIS-CAT ID (NCID)
    AA12014085
  • Text Lang
    ENG
  • Article Type
    departmental bulletin paper
  • ISSN
    1349-6115
  • Data Source
    IR 
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