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. © Springer-Verlag Berlin Heidelberg and IEEE/CS 2001.
CITATION STYLE
Hendriks, C. L. L., & van Vliet, L. J. (2001). Segmentation-free estimation of length distributions using sieves and RIA morphology. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2106, 398–406. https://doi.org/10.1007/3-540-47778-0_38
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