Edge detection considered as very important and fundamental tool in image processing. An image edge is a very sensitive place where the image information and details mostly placed on it. Different filters were used to detect and enhance these edges to improve the sharpness and raising the image clarity. The significance of this paper comes from the study, compare and evaluate the effects of three well-known edge detection techniques in a spatial domain, where this evaluation was performed using both subjective and objective manner to find out the best edge detection algorithm. The Sobel, Homogeneity and Prewitt algorithms were used on 2D gray-scale synthesis and real images in Jordan using C# programming language. According to the comparative results obtained using the three techniques, it was clearly found that Prewitt and Homogeneity algorithms performance were better than Sobel algorithm. Therefore, Prewitt and Homogeneity algorithms can be recommended as useful detection tools in edge detection.
References
[1]
Russell, S.J. and Norvig, P. (2016) Artificial Intelligence: A Modern Approach.
[2]
Bae, S.C., Kweon, I.S. and Yoo, C.D. (2002) COP: A New Corner Detector. Pattern Recognition Letters, 23, 1349-1360. https://doi.org/10.1016/S0167-8655(02)00083-1
[3]
Argialas, D. and Campus, Z. (2000) Comparison of Edge Detection and Hough Transform Techniques for the Extraction of Geologic Features. Image Rochester NY, 34, 790-795.
[4]
Canny, J. (1986) A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence.
https://doi.org/10.1109/TPAMI.1986.4767851
[5]
Ziou, D. and Tabbone, S. (1998) Edge Detection Techniques—An Overview.
[6]
Yen, T. (2003) A Qualitative Profile-Based Approach to Edge Detection. PhD Thesis, September 2003.
[7]
Parker, J.R., Jim, R. and Terzidis, K. (2011) Algorithms for Image Processing and Computer Vision. Wiley Pub.
Armenakis, C. and Savopol, F. (2004) Image Processing and GIS Tools for Feature and Change Extraction. XXth Congress of the International Society for Photogrammetry and Remote Sensing, Istanbul, Commission IV, WG IV/7, 6.
[10]
Maini, R. and Sohal, J.S. (2006) Performance Evaluation of Prewitt Edge Detector for Noisy Images. International Journal on Graphics, Vision and Image Processing, 6, 39-46.
[11]
Hueckel, M.H. and H., M. (1973) A Local Visual Operator Which Recognizes Edges and Lines. Journal of the ACM, 20, 634-647. https://doi.org/10.1145/321784.321791
[12]
Nalwa, V.S. and Binford, T.O. (1986) On Detecting Edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, 699-714.
https://doi.org/10.1109/TPAMI.1986.4767852
[13]
Roushdy, M. (2006) Comparative Study of Edge Detection Algorithms Applying on the Grayscale Noisy Image Using Morphological Filter. ICGST-GVIP Journal, 6, 17-23.
[14]
Tagare, H.D. and de Figueiredo, R.J.P. (1994) Reply to “On the Localization Performance Measure and Optimal Edge Detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 108-110. https://doi.org/10.1109/34.273709
[15]
Fram, J.R. and Deutsch, E.S. (1975) On the Quantitative Evaluation of Edge Detection Schemes and Their Comparison with Human Performance. IEEE Transactions on Computers, C-24, 37-42. https://doi.org/10.1109/T-C.1975.224274
[16]
Hou, Z.J. and Wei, G.W. (2002) A New Approach to Edge Detection. Pattern Recognition, 35, 1559-1570. https://doi.org/10.1016/S0031-3203(01)00147-9
[17]
Zheng, S., Liu, J. and Tian, J.W. (2004) A New Efficient SVM-Based Edge Detection Method. Pattern Recognition Letters, 25, 1143-1154.
https://doi.org/10.1016/j.patrec.2004.03.009
[18]
Bowyer, K., Kranenburg, C. and Dougherty, S. (1999) Edge Detector Evaluation Using Empirical ROC Curves Service Productivity and Value Co-Creation View project Social Acceptance of Autonomous Trucks View Project Edge Detector Evaluation Using Empirical ROC Curves.
[19]
Baareh, A.K.M., Smadi, A.M., Freihat, K. and Al-jarrah, A. (2011) Evaluating the Performance of Edge Detection Techniques through Gradient Method. Engineering, 6, 115-127.
[20]
Adlakha, D., Adlakha, D. and Tanwar, R. (2016) Analytical Comparison between Sobel and Prewitt Edge Detection Techniques. International Journal of Scientific & Engineering Research, 7, 1482-1485.
[21]
Prewitt, J. (1970) Object Enhancement and Extraction. In: Picture Processing and Psychophysics, Elsevier Science, Amsterdam, 75-149.
[22]
Dutta, S. and Chaudhuri, B.B. (2009) A Statistics and Local Homogeneity Based Color Edge Detection Algorithm. International Conference on Advances in Recent Technologies in Communication and Computing, Athens, 29-31 October 2009, 546-548. https://doi.org/10.1109/ARTCom.2009.71
[23]
Sahoo, T. and Pine, S. (2016) Design and Simulation of SOBEL Edge Detection Using MATLAB Simulink. International Research Journal of Engineering and Technology, 3, 2395-2356.
[24]
Acharjya, P.P., Das, R. and Ghoshal, D. (2012) A Study on Image Edge Detection Using the Gradients. International Journal of Scientific and Research Publications, 2, 2-6.
[25]
Shrivakshan, G.T. and Chandrasekar, C. (2012) A Comparison of Various Edge Detection Techniques Used in Image Processing. International Journal of Computer Science Issues, 9, 269-276.
[26]
Zhang, K., Zhang, Y., Wang, P., Tian, Y. and Yang, J. (2018) An Improved Sobel Edge Algorithm and FPGA Implementation an Improved Algorithm and FPGA Implementation. Procedia Computer Science, 131, 243-248.
https://doi.org/10.1016/j.procs.2018.04.209
[27]
Wang, S., Ge, F. and Liu, T. (2006) Evaluating Edge Detection through Boundary Detection. EURASIP Journal on Applied Signal Processing, 2006, Article ID: 076278.
