A Robust Approach for Singular Point Extraction Based on Complex Polynomial Model
Abstract
This paper focuses on a general framework for singular point extraction from vector field. We design a new index of singular point based on complex polynomial model. We test our method in the publicly available benchmark dataset of the singular point detection competition (SPD2010). Our algorithm gets the best results and produces large margins compared to the top five algorithms which took part in the public competition. We also compare our algorithm with the state-of-the-art singular point detection algorithm (called "ZPM" method) with the benchmark. The performance of our method is much better than that of the state-of-the-art method.
Cite
Text
Qi and Liu. "A Robust Approach for Singular Point Extraction Based on Complex Polynomial Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014. doi:10.1109/CVPRW.2014.17Markdown
[Qi and Liu. "A Robust Approach for Singular Point Extraction Based on Complex Polynomial Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014.](https://mlanthology.org/cvprw/2014/qi2014cvprw-robust/) doi:10.1109/CVPRW.2014.17BibTeX
@inproceedings{qi2014cvprw-robust,
title = {{A Robust Approach for Singular Point Extraction Based on Complex Polynomial Model}},
author = {Qi, Jin and Liu, Suxing},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2014},
pages = {78-83},
doi = {10.1109/CVPRW.2014.17},
url = {https://mlanthology.org/cvprw/2014/qi2014cvprw-robust/}
}