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), 2024. Published by Science Publishing Group |
Non-extensive Entropy, Edge Detection, Threshold Value, Gray-Scale Images
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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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