Statistical Optimization for Geometric Fitting: TheoreticalAccuracy Bound and High Order Error Analysis

Access this Article

Search this Article

Abstract

A rigorous accuracy analysis is given to various techniques for estimating parameters of geometric models from noisy data for computer vision applications. First, it is pointed out that parameter estimation for vision applications is very different in nature from traditional statistical analysis and hence a different mathematical framework is necessary in such a domain. After general theories on estimation and accuracy are given, typical existing techniques are selected, and their accuracy is evaluated up to higher order terms. This leads to a "hyperaccurate" method that outperforms existing methods.

Journal

  • Memoirs of the Faculty of Engineering, Okayama University

    Memoirs of the Faculty of Engineering, Okayama University 41(1), 73-92, 2007-01

    Faculty of Engineering, Okayama University

Codes

  • NII Article ID (NAID)
    120002308410
  • NII NACSIS-CAT ID (NCID)
    AA10699856
  • Text Lang
    ENG
  • Article Type
    departmental bulletin paper
  • ISSN
    0475-0071
  • Data Source
    IR 
Page Top