Edge Detection of an Image Based on Extended Difference of Gaussian
American Journal of Computer Science and Technology
Volume 2, Issue 3, September 2019, Pages: 35-47
Received: Nov. 24, 2019; Accepted: Dec. 9, 2019; Published: Dec. 20, 2019
Views 379      Downloads 185
Hameda Abd El-Fattah El-Sennary, Faculty of Science, Aswan University, Aswan, Egypt
Mohamed Eid Hussien, Faculty of Science, Aswan University, Aswan, Egypt
Abd El-Mgeid Amin Ali, Faculty of Computers and Information, Minia University, Minia, Egypt
Article Tools
Follow on us
Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. It's also the most important parts of image processing, especially in determining the image quality. There are many different techniques to evaluate the quality of the image. The most commonly used technique is pixel based difference measures which include peak signal to noise ratio (PSNR), signal to noise ratio (SNR), mean square error (MSE), similarity structure index mean (SSIM) and normalized absolute error (NAE).... etc. This paper study and detect the edges using extended difference of Gaussian filter applied on many of different images with different sizes, then measure the quality images using the PSNR, MSE, NAE and the time in seconds.
Edge Detection, Image Quality, Gaussian Filter, Extended Difference of Gaussian, Peak Signal to Ratio, Mean Square Error
To cite this article
Hameda Abd El-Fattah El-Sennary, Mohamed Eid Hussien, Abd El-Mgeid Amin Ali, Edge Detection of an Image Based on Extended Difference of Gaussian, American Journal of Computer Science and Technology. Vol. 2, No. 3, 2019, pp. 35-47. doi: 10.11648/j.ajcst.20190203.11
Copyright © 2019 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.
T. Tang, H. C. Lin, M. Tomizuka, a learning-based framework for robot peg-hole- insertion, proceedings of the ASME 2015 dynamic systems and control conference, 2015, pp. 1-9.
N. dharan, S. prakash, S. Seenuvasamurthi, Iot based real time traffic monitoring and control system for smart cities, IAETSD journal for advanced Research in applied sciences, 5 (4) (2018) 295-306.
A. Sharma, A. Raghuwanshi, V. Sharma, Biometric System- A Review, International Journal of Computer Science and Information Technologies, 6 (5) (2015) 4616-4619.
P. Mishra, G. Saroha, A Study on Video Surveillance System for Object Detection and Tracking, International Conference on “Computing for Sustainable Global Development, 2016, pp. 346-351.
W. sun, G. Xu, P. GonG, S. Liang, Fractal analysis of remotely sense images: A review of methods and applications, International Journal of Remote Sensing, 27 (22) (2006) 4963-4990.
R. Ružarovský, d. Sobrino, R. Holubek, P. Košťál, Automated in-process inspection method in the Flexible production system iCIM 3000, Applied Mechanics and Materials, 693 (2014) 50-55.
F. Ullah, Q. Habib, M. Irfan, K. Yahya, Autonomous Vehicle Guidance and Control using OpenStreetMap and Advanced Integration Techniques, International Journal of Computer Theory and Engineering, 3 (5) (2011) 604 - 607.
D. Impedovo, G. Pirlo, Automatic signature verification: The State of the Art, Ieee Transactions On Systems, 38 (5 ((2008) 609-635.
W. Lee, M. Lewicki, Learning global properties of scene images based on their correlational structures, dap_Lee_W, 2017, pp. 1-16.
W. Y. Ma, B. S Manjunath, EdgeFlow: a Technique for boundary detection and image segmentation, IEEE transactions on image processing, 9 (8) (2000) 1375-1388.
A. Damodaran, F. Troia, C. Visaggio, T. Austin, M. Stamp, A comparison of Static, dynamic, hybrid analysis for malware detection, Journal Of computer Virology And Hacking Techniques, 13 (1) (2017) 1-12.
M. Nosrati, R. Karimi, M. Hariri, K. Malekian, Edge detection techniques in Processing digital images: investigation of canny algorithm and Gabor method, World Applied Programming, 3 (3) (2013) 116-121.
S. bhuiyan, J. khan, A novel approach of edge detection via a fast and adaptive bidimensional empirical mode decomposition method, World Scientific, 2 (2) (2010) 171-192.
T. Qiu, Y. Yan, An autoadaptive edge-detection algorithm for flame and fire image processing, Ieee Transactions On Instrumentation And Measurement, 61 (5) (2012) 1486-1493.
A. O’Toole, T. Vetter, V. Blanz, Three-dimensional shape and two- dimensional surface reflectance contributions to face recognition: an application of three-dimensional morphing, Vision Research./http:// www.elsevier.com /locate/visres, 1999.
C. Nakatani, A. Pollatsek, Viewpoint-dependent recognition of scenes, The Quarterly Journal Of Experimental Psychology, 55A (1) (2002) 115-139.
C. Gentsos, C. Sotiropoulou, S. Nikolaidis, N. Vassiliadis, Real-time canny edge detection parallel implementation for FPGAs, IEEE international Conference On Electronics, Circuits And Systems, 2010.
P. Acharjya, R. Das, D. Ghoshal, Study and comparison of different edge detectors for image segmentation, Global Journal of Computer Science and Technology Graphics & Vision, 12 (13) (2012).
S. Kumar, M. Singh, D. Shaw, Comparative analysis of various edge detection techniques in biometric application, International Journal of Engineering and Technology (IJET), 8 (6) (2016) 2452-2459.
M. student, Reader, Sobel edge detection algorithm, Journal International Of Computer Science And Management Research, 2 (2) (2013) 1578-1583.
M. Hagar, P. Kubince, About edge detection in digital images, Radioengineering, 27 (4) (2018) 919-929.
R. maini, H. Aggarwal, Study and comparison of various image edge detection techniques, International Journal Of Image Processing (IJIP), 3 (1) (2009) 1-11.
T. Xishan, A novel image edge detection algorithm based on prewitt operator and wavelet transform, International Journal Of Advancements In Computing Technology (Ijact), 4 (19) (2012) 73-82.
M. Juneja, P. Sandhu, Performance evaluation of edge detection techniques For images in spatial domain, International Journal Of Computer Theory And Engineering, 1 (5) (2009) 614-621.
C. Xiansheng, An edge detection new algorithm based on laplacian operator, IEEE 3D international conference on communication software and networks, 2011.
M. Souden, J. Benesty, S. Affes, On optimal frequency domain multichannel linear filtering for noise reduction, IEEE Transactions On Audio, Speech, And Language Processing, 18 (2) (2010) 260-276.
J. Weickert, Applications of nonlinear diffusion in image processing and computer vision, Proceedings Of Algoritmy, LXX (1) (2001) 33-50.
H. Kong, H. Akakin, S. Sarma, A generalized laplacian of Gaussian filter for blob detection and its applications, IEEE Transactions On Cybernetics, 43 (6) (2013) 1719-1733.
M. Dubey, S. Agrawal, An analysis of energy efficient Gaussian filter architectures, International Research Journal of Engineering and Technology 04 (01) (2017)1391-1397.
S. Wang, W. Li, Y. Wang, Y. Jiang, S. Jiang, R. Zhao, An improved difference of Gaussian filter in face recognition, Journal Of Multimedia, 7 (6) (2012) 429- 433.
H. Winnemoller, J. Kyprianidis, S. Olsen, XDoG: an extended difference- of-Gaussians compendium including advanced image stylization, Computers& Graphics, 36 (6) (2012) 740-753.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186