Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
What's in a Set of Points? (Straight Line Fitting)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Describing Complicated Objects by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough techniques for fast optimization of linear constant velocity motion in moving influence fields
Pattern Recognition Letters
Fitting a Second Degree Curve in the Presence of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sub-pixel Bayesian estimation of albedo and height
International Journal of Computer Vision
A Full Bayesian Approach to Curve and Surface Reconstruction
Journal of Mathematical Imaging and Vision
Bayesian modeling of human concept learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Probabilistic Analysis of Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Squares Fitting of Ellipses
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Estimation with Bilinear Constraints in Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting
International Journal of Computer Vision
Algorithms for Matching 3D Line Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Recovery by Integrating over the Joint Image Manifold
International Journal of Computer Vision
Application of the Fisher-Rao Metric to Structure Detection
Journal of Mathematical Imaging and Vision
A simple method for fitting of bounding rectangle to closed regions
Pattern Recognition
Computational Statistics & Data Analysis
Surface reconstruction using local shape priors
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
A Probabilistic Method for Point Matching in the Presence of Noise and Degeneracy
Journal of Mathematical Imaging and Vision
All points considered: a maximum likelihood method for motion recovery
Proceedings of the 11th international conference on Theoretical foundations of computer vision
Conic fitting using the geometric distance
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Journal on Computing and Cultural Heritage (JOCCH)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Ellipse fitting with hyperaccuracy
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We present a simple paradigm for fitting models, parametric and nonparametric, to noisy data, which resolves some of the problems associated with classical MSE algorithms. This is done by considering each point on the model as a possible source for each data point. The paradigm can be used to solve problems which are ill-posed in the classical MSE approach, such as fitting a segment (as opposed to a line). It is shown to be nonbiased and to achieve excellent results for general curves, even in the presence of strong discontinuities. Results are shown for a number of fitting problems, including lines, circles, elliptic arcs, segments, rectangles, and general curves, contaminated by Gaussian and uniform noise.