Work place: Swimburne University of Technology, Melbourne, Australia
E-mail: schandrasekaran@swin.edu.au
Website: https://orcid.org/0000-0003-2871-880X
Research Interests:
Biography
Siva Chandrasekaran is a Senior Lecturer (Computer and Software Engineering), Academic Senate (elected), and Major Discipline Co-Ordinator (Software Engineering) in the School of Science, Computing, and Engineering Technologies at the Swinburne University of Technology. Siva has made significant contributions to engineering education research and published more than 140 scholarly articles in national/international peer-reviewed journals and conference proceedings. Siva is an active researcher, and his research is in Artificial Intelligence, Augmented Reality, Industrial digital transformation, and Engineering practice. Siva conducts world-class research and fosters cross-disciplinary collaboration, and his research is supported by Australian government funding agencies and commercial partners (DFAT, CSIRO, Australian Academy of Science, and the Department of Industry, Science, Energy, and Resources) around Australia.
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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