Matrix analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Describing Complicated Objects by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
The 3L Algorithm for Fitting Implicit Polynomial Curves and Surfaces to Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stable Fitting of 2D Curves and 3D Surfaces by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting Globally Stabilized Algebraic Surfaces to Range Data
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A model based approach for pose estimation and rotation invariant object matching
Pattern Recognition Letters
Improving the stability of algebraic curves for applications
IEEE Transactions on Image Processing
International Journal of Computer Vision
Multilevel algebraic invariants extraction by incremental fitting scheme
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
A coarse-to-fine IP-driven registration for pose estimation from single ultrasound image
Computer Vision and Image Understanding
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Fitting an implicit polynomial (IP) to a data set usually suffers from the difficulty of determining a moderate polynomial degree. An over-low degree leads to inaccuracy than one expects, whereas an overhigh degree leads to global instability. We propose a method based on automatically determining the moderate degree in an incremental fitting process through using QR decomposition. This incremental process is computationally efficient, since by reusing the calculation result from the previous step, the burden of calculation is dramatically reduced at the next step. Simultaneously, the fitting instabilities can be easily checked out by judging the eigenvalues of an upper triangular matrix from QR decomposition, since its diagonal elements are equal to the eigenvalues. Based on this beneficial property and combining it with Tasdizen's ridge regression method, a new technique is also proposed for improving fitting stability.