Abstract
A comparative evaluation of the most commonly used linear methods for edge detection in grayscale images are presented. Detectors based on the first and second derivatives of image brightness are considered. The method for automatic edge tracking in grayscale images is proposed. The model for assessing errors and artifacts caused by sampling during digitization of real input images is proposed. Investigation of edge detectors isotropy and errors caused by input images sampling is conducted. The advantage of the Isotropic operator for edge tracking is shown. The noise immunity of linear edge detection methods is assessed and the superiority of 3 × 3 gradient operators for noisy images is shown. Isotropic and Sobel operators are identified to be optimal on a basis of sampling errors, output noise level, and computational complexity.
Similar content being viewed by others
References
Vision and Mind, Ed. by V. D. Glezer (Nauka, Leningrad, 1993) [in Russian].
Eye Movements and Vision, Ed. by A. L. Yarbus (Nauka, Moscow, 1965) [in Russian].
U. N. Khomyakov, “Shape Expansion Systems,” Radio Electron. TV Tech. 3, 100–111 (1967).
G. Qu, Directional Morphological Gradient Edge Detector, PhD Dissertation (School of Engineering of Santa Clara University, 2001).
V. Mittal, Edge Detection Technique Using Fuzzy Logic, Master of Engineering Dissertation (Thapar University, Patiala, 2008).
R. Maini and H. Aggarwal, “Study and Comparison of Various Image Edge Detection Techniques,” Int. J. Image Processing 3(1), 1–12 (2009).
M. A. Oskoei and H. Hu, A Survey on Edge Detection Methods, Tech. Rep. CES-506 (University of Essex, Colchester, 2010).
S. Abdullah, M. Khalid, R. Yusof, and K. Omar, “License Plate Recognition Using Multi-Cluster and Multilayer Neural Networks,” in Proc. 2nd Int. Conf. on Information and Communication Technologies, ICTTA’06 (Damascus, Apr. 24–28, 2006), Vol. 1, pp. 1818–1823.
Computer Imaging: Digital Image Analysis and Processing, Ed. by S. E. Umbaugh (CRC Press, Taylor and Francis Group, Boca Raton, 2005).
Feature Extraction and Image Processing, Ed. by M. S. Nixon and A. S. Aguado (Acad. Press, Oxford, 2008).
R. Maini and H. Aggarwal, “Study and Comparison of Various Image Edge Detection Techniques,” Int. J. Image Processing 3, 1–12 (2009).
C. S. Panda and S. Patnaik, “Better Edgegap in Grayscale Image Using Gaussian Method,” Int. J. Comput. Appl. Math. 5(1), 53–65 (2010).
J. Yang, R. Yang, S. Li, S.S. Yin, and Q. Qin, “A Novel Edge-Detection Based Segmentation Algorithm for Polarimetric SAR Images,” Int. Arch. Photogrammetry, Remote Sensing Spatial Inf. Sci. 37, 141–144 (2008).
C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 1990; Tekhnosfera, Moscow, 2006).
R. Herpers, M. Michaelis, K.-H. Lichtenauer, and G. Sommer, “Edge and Keypoint Detection in Facial Regions,” in Proc. 2nd Int. Conf. on Automatic Face and Gesture Recognition (Killington, VT, Oct. 14–16, 1996), pp. 212–217.
M. Khomyakov, “Comparative Evaluation of Noise Insensitivity of Linear Edge Detection Techniques,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 21(2), 274–278 (2011).
C. A. Rothwell, J. L. Mundy, W. Hoffman, and V.-D. Nguyen, “Driving Vision by Topology,” in Proc. Int. Symp. on Computer Vision (Coral Gables, 1995), Vol. 37, pp. 395–400.
Author information
Authors and Affiliations
Corresponding author
Additional information
Marat Yur’evich Khomyakov. Born 1985. Graduated from St. Petersburg Electrotechnical University (LETI) in 2008. His specialization is Computer Science. He is currently a graduate student at the Department of Television and Video of SPbETU. His research interests include image processing and biometrics, including face detection, face recognition, and contour tracking methods. Author of four scientific papers.
Rights and permissions
About this article
Cite this article
Khomyakov, M.Y. Comparative evaluation of linear edge detection methods. Pattern Recognit. Image Anal. 22, 291–302 (2012). https://doi.org/10.1134/S1054661812020058
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1054661812020058