Abstract
Gaussian filter is used to smooth an input image to prevent false edge detection caused by image noises in the classic LoG edge detector, but it weakens the image features at the same time which results in some edges cannot be detected efficiently. To ameliorate, this paper presents an improved, feature-centric LoG approach for edge detection. It firstly uses non-local means filter based on structural similarity measure to replace Gaussian filter to smooth an input image which enables the image features to be preserved better, and then image edges can be extracted efficiently by the zero-crossing method for the smoothed image operated by Laplacian operator. Experimental results show that the proposed method can improve the edge detection precision of the classic LoG edge detector, and the non-local means filter used in the presented method achieves better results than the other two typical filters with edge-preserving ability.
J. Hu—This work is supported by National Natural Science Foundation of China (61202261), Scientific and Technological Development Plan of Jilin Province (20130522113JH) and the 13th Five-Year Scientific and Technological Research Foundation of Education Department of Jilin Province(No. 97 in 2016).
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Hu, J., Tong, X., Xie, Q., Li, L. (2016). An Improved, Feature-Centric LoG Approach for Edge Detection. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9787. Springer, Cham. https://doi.org/10.1007/978-3-319-42108-7_36
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DOI: https://doi.org/10.1007/978-3-319-42108-7_36
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