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dc.contributor.authorHasan, Md Junayed
dc.contributor.authorUddin, Jia
dc.contributor.authorPinku, Subroto Nag
dc.identifier.citationHasan, M. J., Uddin, J., & Pinku, S. N. (2017). A novel modified SFTA approach for feature extraction. Paper presented at the 2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016, 10.1109/CEEICT.2016.7873115en_US
dc.descriptionThis conference paper was published in the IEEE Xplore [© 2017 IEEE] and the definite version is available at : The Journal's website is at:
dc.description.abstractTo 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.en_US
dc.publisher© 2016 IEEEen_US
dc.subjectMultilevel thresholingen_US
dc.subjectOtsu functionen_US
dc.subjectSFTA (Segmentation Based Fractal Texture Analysis)en_US
dc.titleA novel modified SFTA approach for feature extractionen_US
dc.typeConference paperen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University

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