A New Uncertainty Measure and Application to Image Processing

Authors

  • T.K Sheeja Associate Professor of Mathematics, T. M. Jacob Memorial Government College, Manimalakunnu, Kerala, India
  • Sunny Kuriakose A Professor and Dean, Federal Institute of Science and Technology, Angamaly, Kerala, India

DOI:

https://doi.org/10.26438/ijcse/v9i4.2024

Keywords:

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

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.

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Published

2021-04-30
CITATION
DOI: 10.26438/ijcse/v9i4.2024
Published: 2021-04-30

How to Cite

[1]
T. Sheeja and S. Kuriakose A, “A New Uncertainty Measure and Application to Image Processing”, Int. J. Comp. Sci. Eng., vol. 9, no. 4, pp. 20–24, Apr. 2021.

Issue

Vol. 9 No. 4 (2021): IJCSE April Edition

Section

Research Article

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