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Part of the book series: Computational Imaging and Vision ((CIVI,volume 5))

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Abstract

The classical grayscale morphological operators erosion and dilation are characterized by strong nonlinearities, a feature that is not desirable in certain image filtering applications. This paper investigates the use of a subclass of nonlinear mean filters, instead of the classical morphological transformation. The nonlinearities of these filters can be hardened or softened progressively, by means of a controlling parameter. The statistical properties of these “soft” morphological filters are investigated. It will be shown that the nonlinear mean filters behave better than or equally well to the grayscale morphological transformations, for certain types of noise.

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References

  1. P. Kuosmanen, “Soft Morphological Filtering”, Report A270, Dept. of Mathematical Sciences, Univ. of Tampere, Finland, April 1993.

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  2. I. Pitas and A. N. Venetsanopoulos, “Nonlinear Mean Filters in Image Processing”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. ASSP-34, No. 34, pp. 573–584, June 1986.

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  3. M. Pappas and I. Pitas, “Grayscale Morphology Using Nonlinear L P Mean Filters”, In Proc. 1995 IEEE Workshop on Nonlinear Signal and Image Processing (NSIP’95), Vol. 1, pp. 34–37, Neos Marmaras, Halkidiki, Greece, 1995.

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© 1996 Kluwer Academic Publishers

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Pappas, M., Pitas, I. (1996). Soft Morphological Operators Based on Nonlinear L P Mean Operators. In: Maragos, P., Schafer, R.W., Butt, M.A. (eds) Mathematical Morphology and its Applications to Image and Signal Processing. Computational Imaging and Vision, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0469-2_22

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  • DOI: https://doi.org/10.1007/978-1-4613-0469-2_22

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-8063-4

  • Online ISBN: 978-1-4613-0469-2

  • eBook Packages: Springer Book Archive

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