Shanthi P.

Work place: SASTRA Deemed to be University, Thanjavur, India

E-mail: shanthip@cse.sastra.ac.in

Website: https://orcid.org/0000-0001-7995-9991

Research Interests:

Biography

P. Shanthi, Assistant Professor, SASTRA Deemed to be University – PhD in Computer Science and Engineering, SASTRA. Bio – Shanthi graduated with a Masters in Industrial Engineering from Anna University, Chennai and obtained her PhD from SASTRA University. She completed her Bachelors in Computer Science and Engineering at Shanmugha College of Engineering, Thanjavur. She has 16 years of teaching experience. Her research interests include Cloud computing, Information security, High performance computing and Quantum computing.

Author Articles
A Novel Local Adaptive Percentage Split Distribution Method for Image Binarization and Classification

By Joy Christy A. Umamakeswari A. Shanthi P. Srilakshmi A. Siva Chandrasekaran

DOI: https://doi.org/10.5815/ijigsp.2025.02.02, Pub. Date: 8 Apr. 2025

Binary thresholding methods separate image pixels into two groups as 0s or 1s. The two types of binary thresholding methods are global thresholding and local thresholding. Global thresholding methods are appropriate for binarizing the images that has smooth and contrast distribution of pixels. The performance of global thresholding struggles with distorted and tampered images as it introduces additional noise and causes variation in contrast and illumination. Local adaptive thresholding methods address the issue with every pixel a threshold based on the contrast distribution of neighboring pixels. This paper introduces Local Adaptive Percentage Split Distribution (LAPSD) method for binarization. LAPSD computes threshold based on percentage wise split of neighboring pixels. The performance of LAPSD is compared with benchmark binary thresholding methods such Bradley’s, Niblack’s, and Sauvola’s against PSNR, SSIM and MSE metrics. The accuracy of LAPSD image binarization is measured using Convolution Neural Network (CNN) models and the results prove that the performance of the proposed method surpasses traditional methods in all means.  

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