Dr Md Junayed Hasan j.hasan@rgu.ac.uk
Research Fellow A
To increase the efficiency of conventional Segmentation Based Fractal Texture Analysis (SFTA), we propose a new approach on SFTA algorithm. We use an optimum multilevel thresholding hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO with the optimization technique for classification based on grey level range to get more accurate output. Experimental results show that proposed approach exhibits average 2% higher classification accuracy than conventional SFTA for our tested dataset.
HASAN, M.J., UDDIN, J. and PINKU, S.N. 2016. A novel modified SFIA approach for feature extraction. In Proceedings of 3rd International conference on electrical engineering and information and communication technology 2016 (iCEEiCT 2016), 22-24 September 2016, Dhaka, Bangladesh. Piscataway: IEEE [online], article 7873115. Available from: https://doi.org/10.1109/CEEICT.2016.7873115
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 3rd International conference on electrical engineering and information and communication technology 2016 (iCEEiCT 2016) |
Start Date | Sep 22, 2016 |
End Date | Sep 24, 2016 |
Acceptance Date | Aug 10, 2016 |
Online Publication Date | Sep 24, 2016 |
Publication Date | Mar 9, 2017 |
Deposit Date | May 16, 2022 |
Publicly Available Date | Jun 7, 2022 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
ISBN | 9781509029068 |
DOI | https://doi.org/10.1109/CEEICT.2016.7873115 |
Keywords | SFTA (segmentation based fractal texture analysis); Multilevel thresholing; HGAPSO; Otsu function |
Public URL | https://rgu-repository.worktribe.com/output/1669589 |
HASAN 2016 A novel modified SFIA
(2 Mb)
PDF
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
A robust self-supervised approach for fine-grained crack detection in concrete structures.
(2024)
Journal Article
Person recognition based on deep gait: a survey.
(2023)
Journal Article
Rethinking densely connected convolutional networks for diagnosing infectious diseases.
(2023)
Journal Article
Data-driven solution to identify sentiments from online drug reviews.
(2023)
Journal Article
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search