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Efficient and Improved Edge Detection Via a Hysteresis Thresholding Method


Affiliations
1 Center of Excellence for Robotics, Artificial Intelligence and Blockchain, Sukkur IBA University, Sukkur, Pakistan
2 School of Electrical Engineering, Hanyang University ERICA Campus, Ansan, Korea, Democratic People's Republic of
3 Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan
4 Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan
5 Department of Mechatronics Engineering, Hanyang University ERICA Campus, Ansan, Korea, Democratic People's Republic of
 

Hysteresis thresholding is a popular technique for automatic edge detection. However, calculating reasonably high and low thresholds using an unsupervized method remains an issue. Conventional low and high threshold-linking methods sometimes produce noisy edges and fail to detect some obvious edges. Here, a novel edge detection algorithm is proposed that provides efficient calculation of thresholds, and links edge maps for extraction of the final edge map at low complexity. The proposed method suppresses unwanted noisy edges while efficiently preserving obvious edges. The simulation results show that the proposed method provides better results in terms of performance and computation time. Thus, it can be applied to any feature imageanalysed by an edge detector.

Keywords

Edge Detection, Hysteresis Method, Low and High Thresholds, Noise Suppression.
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  • Zhang, K., Zhang, Y., Wang, P., Tian, Y. and Yang, J., An improved Sobel edge algorithm and FPGA implementation. Procedia Comput. Sci., 2018, 131, 243–248.

  • Lee, D. H., A simple, high performance edge-adaptive deinterlacing algorithm with very low complexity. In IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA, January 2012, pp. 636–637.

  • Khan, S. and Lee, D., Efficient deinterlacing method using simple edge slope tracing. Opt. Eng., 2015, 54, 103108-1–103108-10.

  • Bias, S. and Kale, I., Mobile hardware based implementation of a novel, efficient, fuzzy logic inspired edge detection technique for analysis of malaria infected microscopic thin blood images. Procedia Comput. Sci., 2018, 141, 374–381.

  • Hossain, F., Asaduzzaman, M., Yousuf, M. A. and Rahman, M. A., Dynamic thresholding based adaptive canny edge detection. Int. J. Comput. Appl., 2016, 975, 37–41.

  • Yang, L., Zhao, D., Wu, X., Li, H. and Zhai, J., An improved Prewitt algorithm for edge detection based on noised image. In International Congress on Image and Signal Processing, Shanghai, China, October 2011, vol. 3, pp. 1197–1200.

  • Rosin, P. L., Unimodal thresholding. Pattern Recogn., 2001, 34, 2083–2096.

  • Otsu, N., A threshold selection method from gray-level histo-grams. IEEE Trans. Syst., Man, Cybern., 1979, 9, 62–66.

  • Canny, J., A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 1986, 6, 679–698.

  • Guha, I. and Saha, P. K., A new algorithm for local blur–scale computation and edge detection. In International Symposium on Visual Computing, Las Vegas, NV, USA, November 2018, pp. 598–606.

  • Medina-Carnicer, R., Madrid-Cuevas, F. J., Carmona-Poyato, A. and Muñoz-Salinas, R., On candidates selection for hysteresis thresholds in edge detection. Pattern Recogn., 2009, 42, 1284– 1296.

  • Medina-Carnicer, R., Carmona-Poyato, A., Muñoz-Salinas, R. and Madrid-Cuevas, F. J., Determining hysteresis thresholds for edge detection by combining the advantages and disadvantages of thres-holding methods. IEEE Trans. Image Process., 2009, 19, 165– 173.

  • Rong, W., Li, Z., Zhang, W. and Sun, L., An improved CANNY edge detection algorithm. In IEEE International Conference on Computer Science and Engineering, Qingdao, China, 1 August 2009, pp. 577–582.

  • Gonzaga, A., Method to evaluate the performance of edge detector. In International Conference on Intelligent Systems Design and Applications, Rio de Janeiro, Brazil, 2007, pp. 341–346.

  • Fernández-García, N. L., Carmona-Poyato, A., Medina-Carnicer, R. and Madrid-Cuevas, F. J., Automatic generation of consensus ground truth for the comparison of edge detection techniques. Image Vis. Comput., 2008, 26, 496–511.


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  • Efficient and Improved Edge Detection Via a Hysteresis Thresholding Method

Abstract Views: 488  |  PDF Views: 133

Authors

Sajid Khan
Center of Excellence for Robotics, Artificial Intelligence and Blockchain, Sukkur IBA University, Sukkur, Pakistan
Dong-Ho Lee
School of Electrical Engineering, Hanyang University ERICA Campus, Ansan, Korea, Democratic People's Republic of
Muhammad Asif Khan
Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan
Abdul Rehman Gilal
Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan
Junaid Iqbal
Department of Mechatronics Engineering, Hanyang University ERICA Campus, Ansan, Korea, Democratic People's Republic of
Ahmad Waqas
Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan

Abstract


Hysteresis thresholding is a popular technique for automatic edge detection. However, calculating reasonably high and low thresholds using an unsupervized method remains an issue. Conventional low and high threshold-linking methods sometimes produce noisy edges and fail to detect some obvious edges. Here, a novel edge detection algorithm is proposed that provides efficient calculation of thresholds, and links edge maps for extraction of the final edge map at low complexity. The proposed method suppresses unwanted noisy edges while efficiently preserving obvious edges. The simulation results show that the proposed method provides better results in terms of performance and computation time. Thus, it can be applied to any feature imageanalysed by an edge detector.

Keywords


Edge Detection, Hysteresis Method, Low and High Thresholds, Noise Suppression.

References





DOI: https://doi.org/10.18520/cs%2Fv118%2Fi6%2F954-960