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Colour Morphological Distance Ordering based on the Log-Exp-Supremum

arXiv 2503.11329 ↗First posted 14 Mar 2025cs.CVcited by 0

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Abstract (arXiv)

Mathematical morphology, a field within image processing, includes various filters that either highlight, modify, or eliminate certain information in images based on an application's needs. Key operations in these filters are dilation and erosion, which determine the supremum or infimum for each pixel with respect to an order of the tonal values over a subset of the image surrounding the pixel. This subset is formed by a structuring element at the specified pixel, which weighs the tonal values. Unlike grey-scale morphology, where tonal order is clearly defined, colour morphology lacks a definitive total order. As no method fully meets all desired properties for colour, because of this difficulty, some limitations are always present. This paper shows how to combine the theory of the log-exp-supremum of colour matrices that employs the Loewner semi-order with a well-known colour distance approach in the form of a pre-ordering. The log-exp-supremum will therefore serve as the reference colour for determining the colour distance. To the resulting pre-ordering with respect to these distance values, we add a lexicographic cascade to ensure a total order and a unique result. The objective of this approach is to identify the original colour within the structuring element that most closely resembles a supremum, which fulfils a number of desired properties. Consequently, this approach avoids the false-colour problem. The behaviour of the introduced operators is illustrated by application examples of dilation and closing for synthetic and natural images.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 22 references that are in the map. First posted on arXiv 14 Mar 2025.

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References · 3

  1. 2017Blind estimation of white Gaussian noise variance in highly textured imagescited by 6
  2. 2023An Approach to Colour Morphological Supremum Formation using the LogSumExp Approximationcited by 2
  3. 2024Matrix-Valued LogSumExp Approximation for Colour Morphologycited by 1

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