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Segmentation-Free Estimation of Length Distributions Using Sieves and RIA Morphology

  • Cris L. Luengo Hendriks
  • Lucas J. van Vliet
Conference paper
Part of the Lecture Notes in Computer Science 2106 book series (LNCS, volume 2106)

Abstract

Length distributions can be estimated using a class of morphological sieves constructed with a so-called Rotation-Invariant, Anisotropic (RIA) morphology. The RIA morphology can only be computed from an (intermediate) morphological orientation space, which is produced by a morphological operation with rotated versions of an anisotropic structuring element. This structuring element is defined as an isotropic structuring element. This structuring element is defined as an isotropic region in a subspace of the image space (i.e. it has fewer dimensions than the image). A closing or opening in this framework discriminates on various object lengths, such as the longest or shortest internal diameter. Applied in a sieve, they produce a length distribution. This distribution is obtained from grey-value images, avoiding the need for segmentation. We apply it to images of rice kernels. The distributions thus obtained are compared with measurements on binarized objects in the same images.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Cris L. Luengo Hendriks
    • 1
  • Lucas J. van Vliet
    • 1
  1. 1.Pattern Recognition GroupDelft University of TechnologyDelftThe Netherlands

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