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arXiv · 2509.06577

Approximating Condorcet Ordering for Vector-valued Mathematical Morphology

Marcos Eduardo Valle·Santiago Velasco-Forero·Joao Batista Florindo·Gustavo Jesus Angulo
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Abstract

Mathematical morphology provides a nonlinear framework for image and spatial data processing and analysis. Although there have been many successful applications of mathematical morphology to vector-valued images, such as color and hyperspectral images, there is still no consensus on the most suitable vector ordering for constructing morphological operators. This paper addresses this issue by examining a reduced ordering approximating the Condorcet ranking derived from a set of vector orderings. Inspired by voting problems, the Condorcet ordering ranks elements from most to least voted, with voters representing different orderings. In this paper, we develop a machine learning approach that learns a reduced ordering that approximates the Condorcet ordering. Preliminary computational experiments confirm the effectiveness of learning the reduced mapping to define vector-valued morphological operators for color images.

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Marcos Eduardo Valle, Santiago Velasco-Forero, Joao Batista Florindo, Gustavo Jesus Angulo. 2025-09-08. Approximating Condorcet Ordering for Vector-valued Mathematical Morphology. https://arxiv.org/abs/2509.06577

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