Contribution to the Prediction of Performances of the Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
The three conditions of a good line parameterization
Pattern Recognition Letters
Pattern Recognition Letters
Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Parameter Estimation in Computer Vision
SIAM Review
A mixture model for pose clustering
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Detecting M-Dimensional Objects in N-Dimensional Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Images to Models for Registration and Object Detection via Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rollin' Justin: mobile platform with variable base
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
| Hi-index | 0.10 |
Parameter clustering is a popular robust estimation technique based on location statistics in a parameter space where parameter samples are obtained from data samples. A problem with clustering methods is that they produce estimates not invariant to transformations of the parameter space. This article presents three contributions to the theoretical study of parameter clustering. First, it introduces a probabilistic formalization of parameter clustering. Second, it uses the formalism to define consistency in terms of a symmetry requirement and to derive criteria for a consistent choice of parameterization. And third, it applies the criteria to the practically relevant cases of motion and pose estimation of three-dimensional shapes. Bias and error statistics on random data sets demonstrate a significant advantage of using a consistent parameterization for rotation clustering. Moreover, clustering parameters of analytic shapes is discussed and a real application example of circle estimation given.