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Unsupervised Image Thresholding using Fuzzy Measures

by M Seetharama Prasad, T Divakar, B Srinivasa Rao, Dr C Naga Raju
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 2
Year of Publication: 2011
Authors: M Seetharama Prasad, T Divakar, B Srinivasa Rao, Dr C Naga Raju
10.5120/3273-4449

M Seetharama Prasad, T Divakar, B Srinivasa Rao, Dr C Naga Raju . Unsupervised Image Thresholding using Fuzzy Measures. International Journal of Computer Applications. 27, 2 ( August 2011), 32-41. DOI=10.5120/3273-4449

@article{ 10.5120/3273-4449,
author = { M Seetharama Prasad, T Divakar, B Srinivasa Rao, Dr C Naga Raju },
title = { Unsupervised Image Thresholding using Fuzzy Measures },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 2 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 32-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number2/3273-4449/ },
doi = { 10.5120/3273-4449 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:46.453751+05:30
%A M Seetharama Prasad
%A T Divakar
%A B Srinivasa Rao
%A Dr C Naga Raju
%T Unsupervised Image Thresholding using Fuzzy Measures
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 2
%P 32-41
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image Thresholding is a necessary task in many image processing applications. In this paper we derive fuzzy rules for π-function. We use π-function to fuzzify the original image; this is constructed to locate the intensities of the misclassification regions. Based on information theory, it maximizes the information between image foreground and background. The merit of using fuzzy set is its ability to handle uncertainty and its robustness. This technique is to optimize the image threshold by effective selection of Region Of Interest (ROI). In general Valley seeking approaches are utilized to select a threshold if the histogram is bimodal. However, histograms would not be bimodal. The fuzzy region range of the π-function is chosen as one standard deviation of the arithmetic mean (μ± σ). Because, the fuzzy region is spread on both sides of the image mean and the non-fuzzy data is located outside of this region. The limitation with the parent version is semi supervised, for low contrast images human perception is required. There exists no unsupervised appropriate procedure in literature to address this problem. The proposed method successfully segments the images of bimodal and multi-model histograms. The experimental results confirm the superiority of the proposed method over existing methods in performance. Our method produces more accurate and reliable results compared to the parent algorithm. This claim has been verified with some experimental trials using all categories of real world images.

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Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Threshold Fuzzy measure Region of Interest SQC

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