A new construction of an image edge detection mask based on Caputo–Fabrizio fractional derivative
Visual Computer, ISSN: 0178-2789, Vol: 37, Issue: 6, Page: 1545-1557
2021
- 11Citations
- 8Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In recent years, the research on image processing based on fractional calculus has attracted much attention. In this work, we proposed a new way to construct an image edge detection mask based on the fractional-order derivative using the Caputo–Fabrizio formulation. The proposed mask was experimented on a large dataset of natural images in both noiseless and noisy situations. In comparison with both classical and fractional edge detectors, the achieved results demonstrated the advantageous performances of the proposed edge detector.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85088805053&origin=inward; http://dx.doi.org/10.1007/s00371-020-01896-4; https://link.springer.com/10.1007/s00371-020-01896-4; https://link.springer.com/content/pdf/10.1007/s00371-020-01896-4.pdf; https://link.springer.com/article/10.1007/s00371-020-01896-4/fulltext.html; https://dx.doi.org/10.1007/s00371-020-01896-4; https://link.springer.com/article/10.1007/s00371-020-01896-4
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know