Nonlocal-means-based smallest univalue segment assimilating nucleus edge detector

Yi Zhan; Mingyue Ding; Xuming Zhang

doi:10.1117/1.JEI.22.1.013023

Abstract

To improve the antinoise performance of the smallest univalue segment assimilating nucleus (SUSAN) edge detector, a nonlocal means-based SUSAN edge detector is proposed. The proposed method first determines the initial SUSAN edge response based on the image patch convolved with an adaptive kernel instead of the single pixel. Then it computes the final edge response using the weighted sum of the initial edge responses of the pixels with their structures similar to the considered pixel. Extensive simulations on natural and real images demonstrate that compared with state-of-the-art detectors, the proposed method performs much better in terms of robustness to noise and edge detection and it provides significantly higher values of Pratt’s figure of merit and performance measure.

Citation Download Citation

Yi Zhan, Yi Zhan, Mingyue Ding, Mingyue Ding, Xuming Zhang, Xuming Zhang, } "Nonlocal-means-based smallest univalue segment assimilating nucleus edge detector," Journal of Electronic Imaging 22(1), 013023 (8 February 2013). https://doi.org/10.1117/1.JEI.22.1.013023 . Submission:

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