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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gonzalez, R., Woods, R.: Digital Image Processing. Publishing house of electronics industry, Beijing (2011)
Teng, J., Zhou, Y., Yan, J., Yang, Y.: The research of serial morphological filters based on multiple structuring elements. J. Northeast Dianli Univ. 28, 38–41 (2008)
Song, Q., Zhou, L.: An improved approach to denoising of image based on wavelet threshold and its application on the gas-liquid two-phase flow. J. Northeast Dianli Univ. 32, 41–44 (2012)
Trier, Q.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition-a survey. Pattern Recogn. 29, 641–662 (2010)
Moshe, P., Alexander, T.: Exact optimality of the shiryaev-roberts procedure for detecting changes in distributions. In: International Symposium on Information Theory and its Applications, pp. 1–6 (2008)
Yasri, I., Hamid, N.H.: Performance analysis of FPGA based Sobel edge detection operator. In: International Conference on Electronic Design, vol. 10, pp. 1–4 (2008)
Maini, R., Sohal, J.S.: Performance evaluation of prewitt edge detector for noisy image. Int. J. Comput. Appl. 9, 39–46 (2006)
Tomsi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of Sixth International Conference on Computer Vision (ICCV 1998), pp. 839–846. IEEE Computer Society, Washington, D.C. (1998)
Prudhvi, V., Venkateswarlu, V.: Ultrasound medical image denoising using hybrid bilateral filtering. Int. J. Comput. Appl. 56, 44–51 (2012)
Farbman, Z., Fattal, R., Lischinsk, D.: Edge-preserving decompositions for multi-Scale tone and detail manipulation. ACM Trans. Graph. (TOG) 27, 67–71 (2008). ACM
Xu, L., Lu, C., Xu, Y.: Image smoothing via \(L_0\) gradient minimization. ACM Trans. Graph 30, 174–177 (2011)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65 (2005)
Peter, J.D., Govindan, V.K., Mathew, A.T.: Robust estimation approach for NL-means filter. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 571–580. Springer, Heidelberg (2008)
Salin, M.E., Zhang, X., Ding, M.: Two modifications of weight calculation of the non-local means denoising method. Engineering 5, 522–526 (2013)
Wang, Z., Bovik, A.H., Sheikh, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-42108-7_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42107-0
Online ISBN: 978-3-319-42108-7
eBook Packages: Computer ScienceComputer Science (R0)