Scholar: National School of Leadership

Research Articles

Vol. 10 No. 2.0 (2021): Scholar : National School of Leadership

Fuzzy Rule Based Bifurcation of Noise

Submitted
November 27, 2022
Published
December 31, 2021

Abstract

 Muzzle  patterns   of   animals  are  uneven  features  of  their  skin  surface.  They  are  unique for every animal  like  finger  prints  of human.  Hence  these  muzzle  patterns  can  be  used  to  identify cattle.  Noise  is  any  unwanted  component  in  an  image.  It  is   important  to  segregate  noise  in  the  of pattern  recognition.  This  paper  propound  a method  for the segregation of  salt  and  pepper  noise  based  on mathematical  ways  using  fuzzy  rules  from  muzzle  images. The  proposed  Fuzzy  operator  consists  of  two  modules  viz. Detection  module  and   Adaptation   module.   A  triangular  shaped  fuzzy  set  described   by   a   two   parameter   membership  function   is   used for fuzzy  reasoning.   13  fuzzy  rules   are  applied  for  each  pixel element  and   an   output   is induced  in  detection  module.  In  adaptation  module   is   further  reduced  and  it  is  added  with  input  pixel  value  to  get  output  pixel  value.  This    method  is  able   to   perform  a  very  strong  noise  cancellation while  preserving  muzzle  image  details.  Various non-linear  filters  such  as   Median  Filter,  SDROM  Filter,  PSM Filter is compared with Fuzzy  Filter and  is  getting  better  results  in  terms  of  PSNR  values  and  SSIM  Values.

References

  1. B. Barry, U. A. Gonzales-Barron, K. McDonnell, S. Ward, “Using muzzle pattern recognition as a biometric approach for cattle identification”, American Society of Agricultural and Biological Engineers, ISSN 0001-2351, vol. 50.n0. 3, 2007.
  2. J. M. Marchant, “Secure Animal Identification and Source Verification “, Fort Collins, Colo.: Optibrand Ltd, 2002.
  3. Alan C Bovik,”Handbook of Image Video Processing”, Elsevier Academic Press Series in Communications, Networking and Multimedia, Editor 2005.
  4. A. K. Jain, “Fundamentals of Digital Image Processing”, Prentice- Hall of India Private Limited, New Delhi, 2002.
  5. C. T. Lin, C.S. George Lee, “Neural Fuzzy Systems”, Prentice- Hall International, Inc.
  6. E. Ahreu and S. K. Mitra, “A signal-dependent rank ordered mean(SDROM)1ter- A new approach for removal of impulse from highly corroupted images”, in Proc. Int. Conf. Acoust. Speech Signal Processing Detroit, MI, vol 4, May 1995, pp. 2371 - 2374
  7. F Russo and G Rumponi,” Non-linear fuzzy operators for image processing”, Signal Processing vol. 38, pp 429-440, Aug 1994.
  8. George. J Klir and Bo Yuan,”Fuzzy Sets and Fuzzy Logic”, PRENTICE Hall of India Private Limited 2010.
  9. Milon Sonka, Vaclav Hlavac, Roger Boyle,” Image Processing, Analysis and Machine Vision”, Second Edition, PWS Publishing.
  10. Rafel. C.Gonzalez and Richard. E. Woods,”Digital Image Processing”, Second Edition, Pearson Education, inc., 2002.
  11. Timothy. J. Ross,”Fuzzy Logic With Engineering Applications”, Mc Graw-Hill, Inc.
  12. William. K. Pratt,”Digital Image Processing”, Third Edition, John Wiley Sons Publications Asia, INC 2004.
  13. Z. Wang and D. Zhang, “Progressive switching median iter for the removal of impulse noise from highly corrupted images”, IEEE Trans. Circuits and Syst. II, Analog and Digital Signal Processing, vol. 46, pp.78-80, January 1999.
  14. Z. Wang and etal, “Image quality assessment: From error visibility to structural similarity”, IEEE Transactions on Image Processing, vol.13, no. 4, pp. 600-612, Apr. 2004.

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