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

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    5 Citations (Scopus)

    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 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.
    Original languageUndefined/Unknown
    Title of host publicationThird International Conference, Scale-Space 2001.
    EditorsM Kerckhove
    Place of PublicationBerlin
    PublisherSpringer
    Pages389-406
    Number of pages18
    ISBN (Print)3-540-42317-6
    Publication statusPublished - 2001
    EventThird International Conference, Scale-Space 2001, Vancouver, Canada. - Berlin
    Duration: 7 Jul 20018 Jul 2001

    Publication series

    Name
    PublisherSpringer Verlag
    NameLecture Notes in Computer Science
    Volume2106
    ISSN (Print)0302-9743

    Conference

    ConferenceThird International Conference, Scale-Space 2001, Vancouver, Canada.
    Period7/07/018/07/01

    Keywords

    • ZX Int.klas.verslagjaar < 2002

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