A Comparison Between the Standard Hough Transform and the Mahalanobis Distance Hough Transform
Abstract
The Hough Transform is a class of medium-level vision techniques generally recognised as a robust way to detect geometric features from a 2D image. This paper presents two related techniques. First, a new Hough function is proposed based on a Mahalanobis distance measure that incorporates a formal stochastic model for measurement and model noise. Thus, the effects of image and parameter space quantisation can be incorporated directly. Given a resolution of the parameter space, the method provides better results than the Standard Hough Transform (SHT), including under high geometric feature densities. Secondly, Extended Kalman Filtering is used as a further refinement process which achieves not only higher accuracy but also better performance than the SHT. The algorithms are compared with the SHT theoretically and experimentally.
Cite
Text
Xu and Velastin. "A Comparison Between the Standard Hough Transform and the Mahalanobis Distance Hough Transform." European Conference on Computer Vision, 1994. doi:10.1007/3-540-57956-7_9Markdown
[Xu and Velastin. "A Comparison Between the Standard Hough Transform and the Mahalanobis Distance Hough Transform." European Conference on Computer Vision, 1994.](https://mlanthology.org/eccv/1994/xu1994eccv-comparison/) doi:10.1007/3-540-57956-7_9BibTeX
@inproceedings{xu1994eccv-comparison,
title = {{A Comparison Between the Standard Hough Transform and the Mahalanobis Distance Hough Transform}},
author = {Xu, Chengping and Velastin, Sergio A.},
booktitle = {European Conference on Computer Vision},
year = {1994},
pages = {95-100},
doi = {10.1007/3-540-57956-7_9},
url = {https://mlanthology.org/eccv/1994/xu1994eccv-comparison/}
}