Edge detection is an important pre-processing step in image analysis. Best results of image analysis extremely depend on edge detection. Up to now many edge detection methods have been developed such as Prewitt, Sobel, LoG, Canny, etc. But, they are sensitive to noise. In this paper we propose a novel edge detection algorithm for images corrupted with noise based on Exponential Entropy. The performance of our method is compared against other methods by using various images. It is observed that the proposed algorithm displayed superior noise resilience and decrease the computation time compared with standard approaches. The results indicate the accuracy of the proposed edge-detection method over conventional edge-detection methods.
| Published in | International Journal of Theoretical and Applied Mathematics (Volume 2, Issue 2) |
| DOI | 10.11648/j.ijtam.20160202.29 |
| Page(s) | 150-155 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Non-extensive Entropy, Edge Detection, Threshold Value, Gray-Scale Images
| [1] | R. C. Gonzalez and R. E. Woods, "Digital Image Processing.", 3rd Edn., Prentice Hall, New Jersey, USA. ISBN: 9780131687288, 2008. |
| [2] | Marr, D., and Hildreth, E. C., "Theory of edge detection", Proc. of the Royal Society of London, b207, 1980, pp. 187-217. |
| [3] | A. Huertas and G. Medioni, “Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No.5, Sep.1986, 651-664. |
| [4] | R. M. Haralick, “Digital Step Edges from Zero Crossing of Second Directional Derivatives”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. PAMI-6, No1, Jan, 1984, 58-68. |
| [5] | F. Ulupinar and G. Medioni, “Refining Edges Detected by a LoG operator”, Computer vision, Graphics and Image Processing, 51, 1990, 275-298. |
| [6] | J. Canny, "A Computational Approach to Edge Detect", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.PAMI-8, No.6, 1986, 679-698. |
| [7] | Yang Xiao, Zhiguo Cao, Junsong Yuan, "Entropic image thresholding based on GLGM histogram", Pattern Recognition Letters 40 (2014), pp. 47–55. |
| [8] | B. Singh and A. P. Singh, "Edge Detection in Gray Level Images based on the Shannon Entropy," Journal of Computer Science 4 (3), 2008, pp.186-191. |
| [9] | S. E. El-Khamy, I. Ghaleb, N. A. El-Yamany, "Fuzzy edge detection with minimum fuzzy entropy criterion", IEEE Proc. MELECON, pp. 498-503, 2002. |
| [10] | Ahmed El-araby, Hassan Badry Mohamed A. El-Owny, M. Heshmat, A. S. Abdel Rady:"New Algorithm For Edge Detection in Medical Images Based on Minimum Cross Entropy Thresholding". International Journal of Computer Science Issues (IJCSI), Volume 11, Issue 2, No.1, pp. 196-200, 2014. |
| [11] | El-Owny, Hassan Badry Mohamed A.: "A novel non-Shannon edge detection algorithm for noisy image". International Journal of Computer Science and Information Security (IJCSIS), Vol. 11, No. 12, pp. 8-13, 2013. |
| [12] | El-Owny, Hassan Badry Mohamed A.: "Edge Detection in Gray Level Images Based on Non-Shannon Entropy". International Journal on Computer Science and Engineering (IJCSE), Vol. 5, No. 12, pp. 932-939, 2013. |
| [13] | Ahmed El-araby, Hassan Badry Mohamed A. El-Owny, M. Hassaballah, A. S. Abdel Rady, M. Heshmat: "Edge Detection of Noisy Medical Images Based Mixed Entropy". Computer Engineering and Intelligent Systems (CEIS), Vol. 4, No. 13, pp.97-106, 2013. |
| [14] | Shannon, C. E., “A mathematical Theory of Communication”, Int. J. Bell. Syst. Technical, vol.27, pp. 379-423, 1948. |
| [15] | Baljit Singh and Amar Partap Singh, “Edge Detection in Gray Level Images based on the Shannon Entropy”, J. Computer Science, vol.4, no.3, 2008, pp.186-191. |
| [16] | Landsberg, P. T. and Vedral, V. Distributions and channel capacities in generalized statistical mechanics. Physics Letters A, 247, (3), 1998, pp. 211-217. |
| [17] | N. R. Pal and S. K. Pal: Object background segmentation using new definitions of entropy, IEEE Proc. 366 (1989), 284–295. |
| [18] | T. Pun, "A new method for gray-level picture thresholding using the entropy of histogram", Signal Process. 2, 1980, pp. 223–237. |
| [19] | J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, "A new method for gray-level picture thresholding using the entropy of histogram", Comput. Graph. Vis. Image Process. 29, 1985, pp. 273–285. |
| [20] | N. Pal and S. Pal, "Entropic thresholding", Signal Process. 16, 1989, pp.97–108. |
| [21] | C.-I. Chang, K. Chen, J. Wang, and M. Althouse, "A relative entropy-based approach to image thresholding", Pattern Recog. 29, 1994, pp. 1275–1289. |
| [22] | A. S. Abutaleb, "Automatic thresholding of gray-level pictures using two-dimensional entropy" Computer Vision, Graphics, and Image processing 47, 1989, pp. 22–32. |
APA Style
Hassan Badry Mohamed El-Owny. (2017). Exponential Entropy Approach for Image Edge Detection. International Journal of Theoretical and Applied Mathematics, 2(2), 150-155. https://doi.org/10.11648/j.ijtam.20160202.29
ACS Style
Hassan Badry Mohamed El-Owny. Exponential Entropy Approach for Image Edge Detection. Int. J. Theor. Appl. Math. 2017, 2(2), 150-155. doi: 10.11648/j.ijtam.20160202.29
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Download@article{10.11648/j.ijtam.20160202.29,
author = {Hassan Badry Mohamed El-Owny},
title = {Exponential Entropy Approach for Image Edge Detection},
journal = {International Journal of Theoretical and Applied Mathematics},
volume = {2},
number = {2},
pages = {150-155},
doi = {10.11648/j.ijtam.20160202.29},
url = {https://doi.org/10.11648/j.ijtam.20160202.29},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20160202.29},
abstract = {Edge detection is an important pre-processing step in image analysis. Best results of image analysis extremely depend on edge detection. Up to now many edge detection methods have been developed such as Prewitt, Sobel, LoG, Canny, etc. But, they are sensitive to noise. In this paper we propose a novel edge detection algorithm for images corrupted with noise based on Exponential Entropy. The performance of our method is compared against other methods by using various images. It is observed that the proposed algorithm displayed superior noise resilience and decrease the computation time compared with standard approaches. The results indicate the accuracy of the proposed edge-detection method over conventional edge-detection methods.},
year = {2017}
}
TY - JOUR T1 - Exponential Entropy Approach for Image Edge Detection AU - Hassan Badry Mohamed El-Owny Y1 - 2017/01/20 PY - 2017 N1 - https://doi.org/10.11648/j.ijtam.20160202.29 DO - 10.11648/j.ijtam.20160202.29 T2 - International Journal of Theoretical and Applied Mathematics JF - International Journal of Theoretical and Applied Mathematics JO - International Journal of Theoretical and Applied Mathematics SP - 150 EP - 155 PB - Science Publishing Group SN - 2575-5080 UR - https://doi.org/10.11648/j.ijtam.20160202.29 AB - Edge detection is an important pre-processing step in image analysis. Best results of image analysis extremely depend on edge detection. Up to now many edge detection methods have been developed such as Prewitt, Sobel, LoG, Canny, etc. But, they are sensitive to noise. In this paper we propose a novel edge detection algorithm for images corrupted with noise based on Exponential Entropy. The performance of our method is compared against other methods by using various images. It is observed that the proposed algorithm displayed superior noise resilience and decrease the computation time compared with standard approaches. The results indicate the accuracy of the proposed edge-detection method over conventional edge-detection methods. VL - 2 IS - 2 ER -
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