Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error Analysis in Stereo Determination of 3-D Point Positions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric computation for machine vision
Geometric computation for machine vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Error propagation in machine vision
Machine Vision and Applications
Artificial Intelligence - Special volume on computer vision
Analysis of error in depth perception with vergence and spatially varying sensing
Computer Vision and Image Understanding
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
Characterizing the uncertainty of the fundamental matrix
Computer Vision and Image Understanding
Computer Vision and Image Understanding
A Framework for Uncertainty and Validation of 3-D RegistrationMethods Based on Points and Frames
International Journal of Computer Vision
On the Optimization Criteria Used in Two-View Motion Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Analysis of 3-D Rotation Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Estimation of Three-Dimensional Rotation and Reliability Evaluation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Estimation with Bilinear Constraints in Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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The bootstrap is a numerical technique, with solid theoretical foundations, to obtain statistical measures about the quality of an estimate by using only the available data. Performance assessment through bootstrap provides the same or better accuracy than the traditional error propagation approach, most often without requiring complex analytical derivations. In many computer vision tasks a regression problem in which the measurement errors are point dependent has to be solved. Such regression problems are called heteroscedastic and appear in the linearization of quadratic forms in ellipse fitting and epipolar geometry, in camera calibration, or in 3D rigid motion estimation. The performance of these complex vision tasks is difficult to evaluate analytically, therefore we propose in this paper the use of bootstrap. The technique is illustrated for 3D rigid motion and fundamental matrix estimation. Experiments with real and synthetic data show the validity of bootstrap as an evaluation tool and the importance of taking the heteroscedasticity into account.