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On the Reliability of Computing Wigner Texture Features

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Abstract

In this work we analyse the robustness of the computation of pseudo-Wigner texture features using both analytic and statistical methods. It is shown that if the input error is normally distributed and the SNR of the input is not very low (i.e. if SNR ≥ 3), then the output error is also normally distributed with known mean and variance. The error distribution in some typical simple signals is considered. The effects of quantisation are also investigated.

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References

  1. S. Barsky, “Performance characterisation of a crack detection algorithm,” School of Electronic Engineering, Information Technology and Mathematics, University of Surrey, Master's Thesis, 1999.

  2. K.R. Castleman, Digital Image Processing, Prentice-Hall: Englewood Chiff, NJ, 1996.

    Google Scholar 

  3. H.I. Christensen and W. F¨orstner, “Performance characterisation of vision algorithms,” Machine Vision and Applications, Vol. 9, No. (5/6), pp. 215–218, 1997.

    Google Scholar 

  4. P. Courtney, N. Thacker, and A. Clark, “Algorithmic modelling for performance evaluation,” Machine Vision and Applications, Vol. 9, No. (5/6), pp. 219–228, 1997.

    Google Scholar 

  5. R.M. Haralick, “Covariance propagation in computer vision,” in Proc. of the Workshop on Performance Characteristics of Vision Algorithms, Cambridge, England, UK, April 1996, pp. 1–12.

  6. G.B.M. Heuvelink, “Uncertainty propagation in GIS,” NCGIA Core Curriculum in Geographic Information Science, University of California Santa Barbara, Technical Report, 1998.

  7. I.S. Gradshteyn and I.M. Ryzhik, Table of Integrals, Series and Products, Academic Press: New York, 1983.

    Google Scholar 

  8. J.F. Kaiser, Digital Filters, Wiley: New York, 1966.

    Google Scholar 

  9. M. Marik, J. Kittler, and M. Petrou, “Error sensitivity assessment of vision algorithms,” IEEE Proc.-Vis. Image Signal Process, Vol. 145, No. 2, pp. 124–130, 1998.

    Google Scholar 

  10. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 3rd ed., McGraw-Hill: New York, 1991.

    Google Scholar 

  11. K.Y. Song, M. Petrou, and J.V. Kittler, “Texture crack detection,” Machine Vision and Applications, Vol. 8. pp. 63–76, 1995.

    Google Scholar 

  12. H. Wechsler, Computational Vision, Academic Press: NewYork, 1990.

    Google Scholar 

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Barsky, S., Petrou, M. On the Reliability of Computing Wigner Texture Features. Journal of Mathematical Imaging and Vision 16, 107–129 (2002). https://doi.org/10.1023/A:1013995330936

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  • DOI: https://doi.org/10.1023/A:1013995330936

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