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Journals / The Eurasia Proceedings of Science Technology Engineering and Mathematics / 2017 / Issue: 1

A NOVEL HYBRID EDGE DETECTION TECHNIQUE: ABC-FA

Elif Deniz Yelmenoglu
Numan Celebi
Tugrul Tasci
Pages
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Publication Date
2017-11-09
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

T he results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Image processing is a vast research field with diversified set of practices utilized in so many application areas such as military, security, medical imaging, machine learning and computer vision based on extracted useful information from any kind of image data.
📊
Key Findings & Data:Edges within images are undoubtedly accepted as one of the most significant features providing substantial practical information for various applications working on top of miscellaneous optimization algorithms to achieve better results.
💡
Conclusion & Impact:T he results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Image processing · edge detection · meta-heuristic

Abstract

Image processing is a vast research field with diversified set of practices utilized in so many application areas such as military, security, medical imaging, machine learning and computer vision based on extracted useful information from any kind of image data. Edges within images are undoubtedly accepted as one of the most significant features providing substantial practical information for various applications working on top of miscellaneous optimization algorithms to achieve better results. Artificial Bee Colony and Firefly algorithms are recently developed optimization algorithms and are used to obtain better results for various problems. In this study, a novel hybrid optimization technique is proposed by combining those algorithms aiming better quality in edge detection on grayscale images. The performance of the proposed algorithm is compared with individual performances of Artificial Bee Colony algorithm and the fundamental edge detection methods . T he results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.

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Cite This Article

Yelmenoglu, E. D., Celebi, N., Tasci, T. (2017). A NOVEL HYBRID EDGE DETECTION TECHNIQUE: ABC-FA. The Eurasia Proceedings of Science Technology Engineering and Mathematics, Issue: 1. https://ajindex.com/articles/1551646

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References (28)

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  18. Gonzalez, R.C., and Woods, R.E. (2007). Digital Image Processing. 3rd ed. Prentice Hall. pp.187-190.
  19. Gonzalez, C. I., Castro, J. R., Melin, P., Castillo, O. (2015). Cuckoo Search Algorithm for the Optimization of Type-2 Fuzzy Image Edge Detection Systems, IEEE Congress on Evolutionary Computation (CEC), pp.449-455.
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  21. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Tech. Rep. TR06, Erciyes University, Kayseri, Turkey. Karaboga, D., Akay, B. (2009). A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation, 214(1):108–132.
  22. Kumar, J. D., Mohan, V. (2014). Edge Detection In The Medical Mr Brain Image Based On Fuzzy Logic Technique, Information Communication and Embedded Systems (ICICES), International Conference on pp.1-9.
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  25. Shashidhara, H. R., Aswath, A. R. (2014). A Novel Approach To Circular Edge Detection For Iris Image Segmentation, Fifth International Conference on Signal and Image Processing, pp.316-320.
  26. Singh, A., Singh, M., Singh, B. (2016). Face Detection and Eyes Extraction Using Sobel Edge Detection and Morphological Operations, Conference on Advances in Signal Processing (CASP), pp.295-300.
  27. Tian, J., Yu, W., Xie, S. (2008). An Ant Colony Optimization Algorithm For Image Edge Detection, IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp.751-756.
  28. Yang, X.S. (2009). Firefly algorithms for multimodal optimization. In: Proceedings of the 5th International Conference on Stochastic Algorithms Foundations and Applications vol. 5792. LNCS Springer pp. 169 – 178. Yigitbasi, E. D., Baykan N. A. (2013). Edge Detection using Artificial Bee Colony Algorithm (ABC), International Journal of Information and Electronics Engineering, pp.634-638.