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Minimum loss of information and image segmentation

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Uncertainty in Knowledge Bases (IPMU 1990)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 521))

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Abstract

A new measure of the information loss in image segmentation is derived from a set of natural properties. A similar quantity can be used in the quantization of a continuous real random n-vector. A new method for thresholding the grey-level histogram of a picture is then introduced. The method is based on the natural requirement of minimum information loss.

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References

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  • Wong, A.K.C. and Sahoo, P.K. (1989) “A Grey-Level Threshold Selection Method Based on Maximum Entropy Principle”, IEEE Trans. on Systems, Man and Cybernetics, Vol. 19, No. 4, July/August 866–871.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Bruno Forte & Vladimir Kolbas

Authors
  1. Bruno Forte
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  2. Vladimir Kolbas
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Editor information

Bernadette Bouchon-Meunier Ronald R. Yager Lotfi A. Zadeh

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© 1991 Springer-Verlag Berlin Heidelberg

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Forte, B., Kolbas, V. (1991). Minimum loss of information and image segmentation. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Uncertainty in Knowledge Bases. IPMU 1990. Lecture Notes in Computer Science, vol 521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028121

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  • DOI: https://doi.org/10.1007/BFb0028121

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54346-6

  • Online ISBN: 978-3-540-47580-4

  • eBook Packages: Springer Book Archive

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