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Implementation of Fuzzy Thresholding for Segmentation of Images

by S. Santhi Kumari, Vamsidhar Enireddy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 2
Year of Publication: 2017
Authors: S. Santhi Kumari, Vamsidhar Enireddy
10.5120/ijca2017915957

S. Santhi Kumari, Vamsidhar Enireddy . Implementation of Fuzzy Thresholding for Segmentation of Images. International Journal of Computer Applications. 180, 2 ( Dec 2017), 46-51. DOI=10.5120/ijca2017915957

@article{ 10.5120/ijca2017915957,
author = { S. Santhi Kumari, Vamsidhar Enireddy },
title = { Implementation of Fuzzy Thresholding for Segmentation of Images },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 180 },
number = { 2 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 46-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number2/28776-2017915957/ },
doi = { 10.5120/ijca2017915957 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:04:22.308892+05:30
%A S. Santhi Kumari
%A Vamsidhar Enireddy
%T Implementation of Fuzzy Thresholding for Segmentation of Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 2
%P 46-51
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Segmentation is a process of dividing an image into multiple regions making it easier for the analysis. It is also an important step in Image processing. It also has number of applications which includes Medical field to analyze a disease, scientific fields including, engineering and technology, face recognition and object. Many algorithms and techniques have been developed for the image segmentation. To remove distinctive areas from a picture an immediate and straight forward method is using thresholding. It looks for a worldwide esteem that boosts the partition between yield classes. Noisy or uneven illuminated images pose a challenge for segmentation. The utilization of solitary hard limit esteem is absolutely the wellspring of imperative division mistakes in numerous situations like boisterous images or uneven illumination. In this paper a multiregion thresholding technique is displayed to overcome the normal downsides of thresholding strategies when images are debased with artifacts and commotion. Pixels from the images are related to various yield centroids by means of fuzzy membership function, evading any underlying hard choice. To make this strategy robust to noise and artifacts it utilizes spatial data through a local aggregation step where the participation level of every pixel is adjusted by neighborhood data that considers the enrollments of the encompassing pixels. The results are then compared with the existing techniques and results obtained are satisfactory.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Processing Segmentation Thresholding fuzzy.

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