Volume 8, Issue 4, August 2020, Page: 109-116
Improved Canny Edge Detector Using Principal Curvatures
Cesar Bustacara-Medina, Department of Systems Engineering, Pontificia Universidad Javeriana, Bogota D. C., Colombia
Leonardo Florez-Valencia, Department of Systems Engineering, Pontificia Universidad Javeriana, Bogota D. C., Colombia
Luis Carlos Diaz, Department of Systems Engineering, Pontificia Universidad Javeriana, Bogota D. C., Colombia
Received: Jun. 30, 2020;       Accepted: Jul. 20, 2020;       Published: Aug. 10, 2020
DOI: 10.11648/j.jeee.20200804.11      View  203      Downloads  163
Abstract
Canny edge detector is a very popular and effective edge feature detector that is used as a preprocessing step in many computer vision algorithms. It is a multi-step detector, which performs smoothing, filtering, non-maximum suppression, followed by a connected-component analysis stage to detect “true” edges, while suppressing “false” non-edge filter responses. Based on the literature, traditional Canny edge detector is sensitive to noise, hence it may lose the weak edge information after noise removal and show poor adaptability of fixed parameters like threshold values. In addition, Canny algorithm tends to over-smooth the noise, resulting in the loss of edge images or pseudo-edges, and the method of selecting thresholds is artificial, and the subjective factors are strong and computationally complex. This paper proposes an improvement to the traditional Canny algorithm by adding curvature information in the non-maximum suppression step (NMS) in order to obtain an accurate edge identification. Additionally, a set of tests and results is presented that show how by adding curvature characteristics to the NMS process, better results are obtained in the edge detection in Canny’s algorithm.
Keywords
Edge Detection, Non-maximum Suppression, Canny Edge Detector, Low-Level Processing
To cite this article
Cesar Bustacara-Medina, Leonardo Florez-Valencia, Luis Carlos Diaz, Improved Canny Edge Detector Using Principal Curvatures, Journal of Electrical and Electronic Engineering. Vol. 8, No. 4, 2020, pp. 109-116. doi: 10.11648/j.jeee.20200804.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
L. Kitchen and A. Rosenfeld, “Edge Evaluation Using Local Edge Coherence,” IEEE Trans. Syst. Man. Cybern., vol. SMC-11, no. 9, pp. 597-605, 1981.
[2]
Y. Li, S. Wang, Q. Tian, and X. Ding, “A survey of recent advances in visual feature detection,” Neurocomputing, vol. 149, pp. 736-751, 2015.
[3]
F. A. Pellegrino, W. Vanzella, and V. Torre, “Edge Detection Revisited,” IEEE Trans. Syst. Man Cybern., vol. 34, no. 3, pp. 1500-1518, 2004.
[4]
T. B. Nguyen and D. Ziou, “Contextual and non-contextual performance evaluation of edge detectors,” Pattern Recognit. Lett., vol. 21, pp. 805-816, 2000.
[5]
M. Basu, “Gaussian-Based Edge-Detection Methods — A Survey,” IEEE Trans. Syst. Man Cybern., vol. 32, no. 3, pp. 252-260, 2002.
[6]
S. Saluja, A. K. Singh, and S. Agrawal, “A Study of Edge-Detection Methods,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 2, no. 1, pp. 994-999, 2013.
[7]
P. Perona and J. Malik, “Detecting and localizing edges composed of steps, peaks and roofs,” 1990.
[8]
J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679-698, 1986.
[9]
J. Canny, “Finding edges and lines in images,” Massachusetts Institute of Technology, 1983.
[10]
W. Mcilhagga, “The Canny Edge Detector Revisited,” Int. J. Comput. Vis., vol. 91, pp. 251-261, 2011.
[11]
J. Shen and S. Castan, “An Optimal Linear Operator for Step Edge Detection,” Graph. Model. Image Process., vol. 54, no. 2, pp. 112-133, 1992.
[12]
B. M. ter Haar Romeny, Geometry-Driven Diffusion in Computer Vision, vol. 1. Springer-Science+Business Media, 1994.
[13]
A. F. Korn, “Toward a Symbolic Representation of Intensity Changes in Images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 10, no. 5, pp. 610-625, 1988.
[14]
J. Weickert, Anisotropic Diffusion in Image Processing. Teubner Stuttgart, 1998.
[15]
T. Lindeberg, “Edge Detection and Ridge Detection with Automatic Scale Selection,” Int. J. Comput. Vis., vol. 30, no. 2, pp. 117-156, 1998.
[16]
A. Neubeck and L. Van Gool, “Efficient non-maximum suppression,” in International Conference on Pattern Recognition, 2006, vol. 3, pp. 850-855.
[17]
M. S. Nixon and A. S. Aguado, “Low-level feature extraction (including edge detection),” in Feature Extraction and Image Processing for Computer Vision, Elsevier Ltd., 2020, pp. 141–222.
[18]
F. Devernay, “A Non-Maxima Suppression Method for Edge Detection with Sub-Pixel Accuracy,” 1995.
[19]
C. Sun and P. Vallotton, “Fast linear feature detection using multiple directional non-maximum suppression,” J. Microsc., vol. 234, no. 2, pp. 147-157, 2009.
[20]
O. Monga, R. Deriche, G. Malandain, and J. P. Cocquerez, “3D edge detection by separable recursive filtering and edge closing,” Proc.-Int. Conf. Pattern Recognit., vol. 1, pp. 652-654, 1990.
[21]
O. Monga, R. Deriche, G. Malandain, and J. P. Cocquerez, “Recursive filtering and edge closing: Two primary tools for 3D edge detection,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 427 LNCS, pp. 56-65, 1990.
[22]
O. Monga, R. Deriche, and J. M. Rocchisani, “3D edge detection using recursive filtering: Application to scanner images,” CVGIP Image Underst., vol. 53, no. 1, pp. 76-87, 1991.
Browse journals by subject