On Three-Dimensional Surface Reconstruction Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Describing Complicated Objects by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Parameterized Families of Polynomials for Bounded Algebraic Curve and Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Bias of Conic Fitting and Renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduction of Bias in Maximum Likelihood Ellipse Fitting
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
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The gradient weighted least-squares criterion is a popular criterion for conic fitting. When the non-linear minimisation problem is solved using the eigenvector method, the minimum is not reached and the resulting solution is an approximation. In this paper, we refine the existing eigenvector method so that the minimisation of the non-linear problem becomes exactly. Consequently we apply the refined algorithm to the re-normalisation approach, by which the new iterative scheme yields to bias-corrected solution but based on the exact minimiser of the cost function. Experimental results show the improved performance of the proposed algorithm.