A General Method for Errors-in-Variables Problems in Computer Vision

The Errors-in-Variables (EIV) model from statistics is often employed in computer vision thoughonlyrarely under this name. In an EIV model all the measurements are corrupted by noise while the a priori information is captured with a nonlinear constraint among the true (unknown) values of these measurements. To estimate the model parameters and the uncorrupted data, the constraint can be linearized, i.e., embedded in a higher dimensional space. We show that linearization introduces data-dependent (heteroscedastic) noise and propose an iterative procedure, the heteroscedastic EIV (HEIV) estimator to obtain consistent estimates in the most general, multivariate case. Analytical expressions for the covariances of the parameter estimates and corrected data points, a generic method for the enforcement of ancillary constraints arising from the underlying geometry are also given. The HEIV estimator minimizes the first order approximation of the geometric distances between the measurements and...

• Type: Conference
• Year: 2000
• Where: CVPR
• Authors: Bogdan Matei, Peter Meer
• Keywords: Computer Vision, CVPR 2000, Earlier Solution, HEIV Estimator, Nonlinear Constraint, True Data Points, Vision Estimation Tasks