Work place: SASTRA Deemed to be University, Thanjavur, India
E-mail: umamakeswari.arumugam@gmail.com
Website: https://orcid.org/0000-0001-9724-1115
Research Interests:
Biography
A. Umamakeswari, Professor and Dean, School of Computing, SASTRA Deemed University, Thanjavur has research experience in the areas of Wireless Sensor Networks, Cloud Computing, Internet of Things, Machine Learning, Embedded Systems, and Block chain. She has been an integral part of completing projects funded by the Government Institutions on content development for Software Testing, Microprocessors & Interfacing (NMEICT-MHRD) and Design of Logic tools for Process Modelling (IGCAR). She has guided research works that feature the development of Intrusion Detection Systems (IDS) for Wireless Sensor Networks & Industrial Cyber-Physical Systems (CPS), computation offloading for Edge environments, Cloud Security, Block chain-based solutions for the Internet of Things and Predicting Air quality in Urban environments using Machine Learning. She has published good number of papers in reputed journals in the areas of Internet of Things, Machine Learning and Cyber Physical Systems.
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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