A Novel Fractional Edge Detector Based on Generalized Fractional Operator
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 GlobalPublication 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