Is covariance information useful in estimating vision parameters?

Abstract

This paper assesses some of the practical ramifications of recent developments in estimating vision parameters given information characterizing the uncertainty of the data. This uncertainty information may sometimes be estimated in association with the observation process, and is usually represented in the form of covariance matrices. An empirical study is made of the conditions under which improved parameter estimates can be obtained from data when covariance information is available. We explore, in the case of fundamental matrix estimation and conic fitting, the extent to which the noise should be anisotropic and inhomogeneous if improvements over traditional methods are to be obtained. Critical in this is the devising of synthetic experiments under which noise conditions can be precisely controlled. Given that covariance information is, in itself, subject to estimation error, testes are also undertaken to determine the impact of imprecise covariance information upon the quality of parameter estimates. We thus investigate the consequences for parameter estimation of inaccuracies in the characteristiziaton of noise that inevitably arise in practical computation.