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A Novel Fractional Edge Detector Based on Generalized Fractional Operator

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journal contribution
submitted on 2024-07-31, 06:00 and posted on 2024-07-31, 12:34 authored by Diaa Diaa Eldin Elgezouli, Mohamed Abdoon, Samir Brahim Belhaouari, Dalal Khalid Almutairi

This work pioneers a novel approach in image edge detection through the utilization of the generalized fractional operator. By harnessing the global attributes inherent in fractional derivatives, it aims to enhance the extraction of intricate edge details.This is accomplished by creating the mask by using fractional derivative and adapt the mask by another parameter, yielding compelling and informative edge representations, as validated by experimental results. This advancement not only augments computer vision and image analysis techniques but also holds promise for refining image processing methodologies. Future endeavors may explore its adaptability across diverse imaging domains like medical and satellite imagery, while integration into deep learning frameworks could elevate its potential for advanced feature extraction and deeper image understanding. Additionally, optimizing its computational efficiency would broaden its scope for real-time deployment in fields such as robotics and autonomous systems.

Other Information

Published in: European Journal of Pure and Applied Mathematics
License: https://creativecommons.org/licenses/by-nc-sa/4.0
See article on publisher's website: https://dx.doi.org/10.29020/nybg.ejpam.v17i2.5141

Funding

Open Access funding provided by Majmaah University.

History

Language

  • English

Publisher

New York Business Global

Publication Year

  • 2024

License statement

This Item is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

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