Using kernel functions to estimate the probability density function for segmentation images

Eesa, Aseel Muslim; Alhasnawi, Mohammad Kaisb Layous; Hamza, Zainab Falih

doi:10.47974/JSMS-1034

Open Access Research Article

Using kernel functions to estimate the probability density function for segmentation images

^*Aseel Muslim EesaCorresponding authoraseel.m.issa@uos.edu.iqFaculty of Administration and Economics University of Sumer Wasit, IraqView full profile → , Mohammad Kaisb Layous Alhasnawizainab.stat@uoitc.edu.iqFaculty of Administration and Economics University of Sumer Wasit, IraqView full profile → , Zainab Falih Hamzamohammadkaisb@uos.edu.iqCollege of Business Informatics University of Information Technology and Communications Baghdad, IraqView full profile →

* Corresponding author · click or hover a name for details

pp. 645–654Vol. 27Issue 3March 2024DOI: 10.47974/JSMS-1034XML

Received:: 07 Feb 2023
Published Online:: 30 Mar 2024
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1034
Pages:: 645–654

Abstract

This paper used nonparametric kernel functions to segment images using the thresholding method. This was done by transforming the image to grey and then estimating the probability density function from the grey image data and taking the highest value of the core function vector as the threshold limit. The study also found that the bandwidth parameter affects the segmentation process because it affects the estimate of the probability density function. The bandwidth parameter smooths the curve and brings it closer to the true curve because of its significant effect on bias and variance. The beam width parameter is known to depend on the size of the sample. Thus, it was found that when the parameter is used to determine the image size into equal rows and columns, it gives good, satisfactory results for segmented images that contain the most important areas of interest with removing unhelpful or important areas. Finally, the uniform Gaussian, cosine, triangular, and logistic Silverman kernel functions proved their efficiency in extracting all image features.

Keywords

Kernel function Segmentation Thresholding Image processing Preamble parameter

Subject Classifications

05C4211R45

References

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