Morphology on categorical distributions
Research output: Book/Report › Report › Research
The categorical distribution is a natural representation of uncertainty in multi-class segmentations. In the two-class case the categorical distribution reduces to the Bernoulli distribution, for which grayscale morphology provides a range of useful operations. In the general case, applying morphological operations on uncertain multi-class segmentations is not straightforward as an image of categorical distributions is not a complete lattice. Although morphology on color images has received wide attention, this is not so for color-coded or categorical images and even less so for images of categorical distributions. In this work, we establish a set of requirements for morphology on categorical distributions by combining classic morphology with a probabilistic view. We then define operators respecting these requirements, introduce protected operations on categorical distributions and illustrate the utility of these operators on two example tasks: modeling annotator bias in brain tumor segmentations and segmenting vesicle instances from the predictions of a multi-class U-Net.
Translated title of the contribution | Morfologi på kategoriske fordelinger |
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Original language | English |
Publisher | arXiv.org |
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Publication status | Published - 14 Dec 2020 |
- cs.CV, Morphology, Categorical distribution
Research areas
Links
- http://arxiv.org/pdf/2012.07315v1
Final published version
ID: 253704279