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A New Uncertainty Measure and Application to Image Processing

Sheeja T.K.1 , Sunny Kuriakose A.2

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-4 , Page no. 20-24, Apr-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i4.2024

Online published on Apr 30, 2021

Copyright © Sheeja T.K., Sunny Kuriakose A. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sheeja T.K., Sunny Kuriakose A., “A New Uncertainty Measure and Application to Image Processing,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.20-24, 2021.

MLA Style Citation: Sheeja T.K., Sunny Kuriakose A. "A New Uncertainty Measure and Application to Image Processing." International Journal of Computer Sciences and Engineering 9.4 (2021): 20-24.

APA Style Citation: Sheeja T.K., Sunny Kuriakose A., (2021). A New Uncertainty Measure and Application to Image Processing. International Journal of Computer Sciences and Engineering, 9(4), 20-24.

BibTex Style Citation:
@article{T.K._2021,
author = {Sheeja T.K., Sunny Kuriakose A.},
title = {A New Uncertainty Measure and Application to Image Processing},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2021},
volume = {9},
Issue = {4},
month = {4},
year = {2021},
issn = {2347-2693},
pages = {20-24},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5320},
doi = {https://doi.org/10.26438/ijcse/v9i4.2024}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i4.2024}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5320
TI - A New Uncertainty Measure and Application to Image Processing
T2 - International Journal of Computer Sciences and Engineering
AU - Sheeja T.K., Sunny Kuriakose A.
PY - 2021
DA - 2021/04/30
PB - IJCSE, Indore, INDIA
SP - 20-24
IS - 4
VL - 9
SN - 2347-2693
ER -

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Abstract

Uncertainty measures form essential constituents of information theory as they provide a sufficient mechanism for determining the quantity of useful information contained in a system. In the present work, the concept of divergence between fuzzy sets are made use of in defining new measures of uncertainty in the framework of fuzzy rough sets. Further, these measures are utilized in developing an algorithm for binary image segmentation of a grey level image. Moreover, the proposed algorithm is implemented using different test images with the help of an OCTAVE program.

Key-Words / Index Term

Divergence, Image segmentation, Fuzzy Set, Rough set, Uncertainty measure

References

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