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All Journal Nusantara Journal of Computers and its Applications
I Gusti Agung Socrates Adi Guna
Informatics Department, Faculty of Information Technology, Sepuluh Nopember Institute of Technology (ITS), Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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ANALYSIS OF ADAPTIF LOCAL REGION IMPLEMENTATION ON LOCAL THRESHOLDING METHOD I Gusti Agung Socrates Adi Guna; Hendra Maulana; Agus Zainal Arifin; Dini Adni Navastara
NJCA (Nusantara Journal of Computers and Its Applications) Vol 1, No 2 (2016): Desember 2016
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v1i2.10

Abstract

Thresholding is a simple and effective technique for image segmentation. Thresholding techniques can begrouped into two categories, global thresholding and local thresholding. All local threshold method generallybegins with determining thresholds in each pixel by checking the area centered on the pixel, using a box shape (x,y) which is fixed by the size of the neighborhood "b". If the neighborhood is very small, then the algorithm will besensitive to noise and excessive segmentation occurs. Whereas, if the size of the neighborhood is very large thenthe algorithm will apply resemble the global threshold method. In this study, we propose a method of calculationof Local Adaptive Region, to determine the value of each pixel that is flexible neighborhoods, where each pixelhas values different neighborhoods based on the value of the standard deviation region. Adaptive method on thelocal region thresholding consists of several processes, namely: Image Enhancement, Adaptive Local Region andthresholding. Based on evaluation of ME, image result of threshold using the Adaptive Local Region method, givingan average ME smallest value, that is 16.99% at Niblack method and 19.46% at Sauvola method. And onevaluation of the RAE, image result of threshold using the Adaptive Local Region method, giving an averageRAE smallest value, that is 15.26% at Niblack method and 25.58% at Sauvola method. In addition, the results oftrials with various noise variance represent that the method of Adaptive Local Region resistant to noise.
Co-Authors Agus Zainal Arifin Dini Adni Navastara, Dini Adni Hendra Maulana