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Journal of Mathematical Imaging and Vision

, Volume 23, Issue 2, pp 175–183 | Cite as

FNS, CFNS and HEIV: A Unifying Approach

  • Wojciech Chojnacki
  • Michael J. Brooks
  • Anton van den Hengel
  • Darren Gawley
Article

Abstract

Estimation of parameters from image tokens is a central problem in computer vision. FNS, CFNS and HEIV are three recently developed methods for solving special but important cases of this problem. The schemes are means for finding unconstrained (FNS, HEIV) and constrained (CFNS) minimisers of cost functions. In earlier work of the authors, FNS, CFNS and a core version of HEIV were applied to a specific cost function. Here we extend the approach to more general cost functions. This allows the FNS, CFNS and HEIV methods to be placed within a common framework.

Keywords

statistical methods maximum likelihood (un)constrained minimisation fundamental matrix epipolar equation conic fitting 

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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Wojciech Chojnacki
    • 1
  • Michael J. Brooks
    • 1
  • Anton van den Hengel
    • 1
  • Darren Gawley
    • 1
  1. 1.School of Computer ScienceUniversity of AdelaideAdelaideAustralia

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