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ANALYSIS OF ADAPTIF LOCAL REGION IMPLEMENTATION ON LOCAL THRESHOLDING METHOD
Abstract
Thresholding is a simple and effective technique for image segmentation. Thresholding techniques can be
grouped into two categories, global thresholding and local thresholding. All local threshold method generally
begins 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 be
sensitive to noise and excessive segmentation occurs. Whereas, if the size of the neighborhood is very large then
the algorithm will apply resemble the global threshold method. In this study, we propose a method of calculation
of Local Adaptive Region, to determine the value of each pixel that is flexible neighborhoods, where each pixel
has values different neighborhoods based on the value of the standard deviation region. Adaptive method on the
local region thresholding consists of several processes, namely: Image Enhancement, Adaptive Local Region and
thresholding. Based on evaluation of ME, image result of threshold using the Adaptive Local Region method, giving
an average ME smallest value, that is 16.99% at Niblack method and 19.46% at Sauvola method. And on
evaluation of the RAE, image result of threshold using the Adaptive Local Region method, giving an average
RAE smallest value, that is 15.26% at Niblack method and 25.58% at Sauvola method. In addition, the results of
trials with various noise variance represent that the method of Adaptive Local Region resistant to noise.
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PDFReferences
Sezgin, Mehmet, Bulent Sankur. 2004. Survey Over Image
Thresholding Techniques and Quantitative PerformanceEvaluation.
Journal of Electronic Imaging.
Singh, O. Imocha, et al. 2012.Local Contrast and Mean
Thresholding in Image Binarization. International Journal of
Computer Applications 51.6.
Firdousi, Rukhsar, and Shaheen Parveen.2014.Local Thresholding
Techniques in Image Binarization. International Journal
of Engineering and Computer Science. Vol 3.
Bieniecki, W. and Grabowski, S., 2005. Multi-pass approach
to adaptive thresholding based image segmentation. In Proceedings
of the 8th International IEEE Conference CADSM.
pp. 418-423.
W.Niblack (1986). An introduction to digital image processing.
–116. Prentice-Hall, Englewood Cliffs,NJ.
Boiangiu, C.A., Olteanu, A., Stefanescu, A., Rosner, D., Tapus,
N. and Andreica, M.I., 2011, May. Local Thresholding
Algorithm Based on Variable Window Size Statistics. In Proceedings
of the 18th International Conference on Control Systems
and Computer Science (CSCS). Vol. 2, pp. 647-652.
C. Tomasi, R. Manduchi, Bilateral Filtering for Gray and
Color Images. IEEE International Conference on Computer
Vision_98, 1998, 836–846.
N. Otsu. 1979.A Threshold Selection Method from Gray-
Level Histograms. Systems, Man and Cybernetics, IEEE
Transactions on, vol. 9, pp. 62-66.
Singh, T.R., Roy, S., Singh, O.I., Sinam, T. and Singh, K.,
A new local adaptive thresholding technique in binarization.
arXiv preprint arXiv:1201.5227.
Agus Zainal Arifin, Akira Asano, Image Segmentation by
Histogram Thresholding using Hierarchical Cluster Analysis.
Pattern Recognition Letters 27, 2006, 1515-1521
DOI: http://dx.doi.org/10.36564/njca.v1i2.10
DOI (PDF): http://dx.doi.org/10.36564/njca.v1i2.10.g10
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