IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric concepts for geometric design
Geometric concepts for geometric design
Analysing error of fit functions for ellipses
Pattern Recognition Letters
Ellipse fitting using orthogonal hyperbolae and Stirling's oval
Graphical Models and Image Processing
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Method for Fitting Parametric and Nonparametric Models to Noisy Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Bias of Conic Fitting and Renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking with the kinematics of extremal contours
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
3D model based pose estimation for omnidirectional stereovision
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Unified Computation of Strict Maximum Likelihood for Geometric Fitting
Journal of Mathematical Imaging and Vision
Minimal representations for uncertainty and estimation in projective spaces
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Hypercatadioptric line images for 3D orientation and image rectification
Robotics and Autonomous Systems
3D model based tracking for omnidirectional vision: A new spherical approach
Robotics and Autonomous Systems
Guaranteed ellipse fitting with the sampson distance
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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
Algorithms for projecting points onto conics
Journal of Computational and Applied Mathematics
| Hi-index | 0.00 |
We consider the problem of fitting a conic to a set of 2D points. It is commonly agreed that minimizing geometrical error, i.e. the sum of squared distances between the points and the conic, is better than using an algebraic error measure. However, most existing methods rely on algebraic error measures. This is usually motivated by the fact that pointto-conic distances are difficult to compute and the belief that non-linear optimization of conics is computationally very expensive. In this paper, we describe a parameterization for the conic fitting problem that allows to circumvent the difficulty of computing point-to-conic distances, and we show how to perform the non-linear optimization process efficiently.