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|Title:||Is covariance information useful in estimating vision parameters?|
Van Den Hengel, A.
|Citation:||Videometrics and optical methods for 3D shape measurement : 22-23 January 2001, San Jose, USA / Sabry F. El-Hakim, Armin Gruen (eds.), pp. 195-203|
|Publisher:||THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS|
|Publisher Place:||PO BOX 10 BELLINGHAM WASHINGTON USA|
|Series/Report no.:||Proceedings of SPIE--the International Society for Optical Engineering ; 4309.|
|Conference Name:||SPIE Conference on Visual Data Exploration and Analysis (2001 : San Jose, California)|
|Michael J. Brooks, Wojciech Chojnacki, Darren Gawley, and Anton van den Hengel|
|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.|
|Description:||© 2003 COPYRIGHT SPIE--The International Society for Optical Engineering|
|Appears in Collections:||Computer Science publications|
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