Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
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
Matrix computations (3rd ed.)
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
The Generic Bilinear Calibration-Estimation Problem
International Journal of Computer Vision
Errors in variables for numerical analysts
Proceedings of the second international workshop on Recent advances in total least squares techniques and errors-in-variables modeling
Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint
International Journal of Computer Vision - Special issue on a special section on visual surveillance
On the Fitting of Surfaces to Data with Covariances
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rationalising the Renormalisation Method of Kanatani
Journal of Mathematical Imaging and Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Optimization Criteria and Geometric Algorithms for Motion and Structure Estimation
International Journal of Computer Vision
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Optimal Estimation of Three-Dimensional Rotation and Reliability Evaluation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Balanced Recovery of 3D Structure and Camera Motion from Uncalibrated Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Heteroscedastic errors-in-variables models in computer vision
Heteroscedastic errors-in-variables models in computer vision
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
From FNS to HEIV: A Link between Two Vision Parameter Estimation Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Balanced Approach to 3D Tracking from Image Streams
ISMAR '05 Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality
IEEE Transactions on Information Theory
Occlusion registration in video-based augmented reality
VRCAI '08 Proceedings of The 7th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
Compact Fundamental Matrix Computation
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Metric Learning for Image Alignment
International Journal of Computer Vision
Highest accuracy fundamental matrix computation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Unified Computation of Strict Maximum Likelihood for Geometric Fitting
Journal of Mathematical Imaging and Vision
Hyper least squares fitting of circles and ellipses
Computational Statistics & Data Analysis
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Image and Vision Computing
Renormalization returns: hyper-renormalization and its applications
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Optimization techniques for geometric estimation: beyond minimization
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
| Hi-index | 0.14 |
In an errors-in-variables (EIV) model, all the measurements are corrupted by noise. The class of EIV models with constraints separable into the product of two nonlinear functions, one solely in the variables and one solely in the parameters, is general enough to represent most computer vision problems. We show that the estimation of such nonlinear EIV models can be reduced to iteratively estimating a linear model having point dependent, i.e., heteroscedastic, noise process. Particular cases of the proposed heteroscedastic errors-in-variables (HEIV) estimator are related to other techniques described in the vision literature: the Sampson method, renormalization, and the fundamental numerical scheme. In a wide variety of tasks, the HEIV estimator exhibits the same, or superior, performance as these techniques and has a weaker dependence on the quality of the initial solution than the Levenberg-Marquardt method, the standard approach toward estimating nonlinear models.