A parabolic detection algorithm based on kernel density estimation

Authors:
Xiaomin Liu;Qi Song;Peihua Li
Affiliations:
School of Computer Science and Technology, Hei Long Jiang University Harbin, Hei Long Jiang Province, China;School of Computer Science and Technology, Hei Long Jiang University Harbin, Hei Long Jiang Province, China;School of Computer Science and Technology, Hei Long Jiang University Harbin, Hei Long Jiang Province, China
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

The traditional Hough transform needs the edge detection in advance, so the effect of edge detection influences the final fitting result. This paper proposes a new method of detecting parabolas using the kernel density estimate based on the theory of Rozenn Dahyot, and extends this method into the eyelid detection in noisy images and other images including parabolas. In our paper, the edge detection is not necessary. On one hand, we not only consider the current points on the parabola, but also ones around the parabola. Experiments demonstrate that the proposed algorithm is robust and insensitive to the noise.