A Multi-parameter Persistence Framework for Mathematical Morphology

• URL: http://arxiv.org/abs/2103.13013v1
• Date: Wed, 24 Mar 2021 06:46:00 GMT
• Title: A Multi-parameter Persistence Framework for Mathematical Morphology
• Authors: Yu-Min Chung, Sarah Day, Chuan-Shen Hu
• Abstract summary: We look at morphological operations through the lens of persistent homology. persistent homology is a tool at the heart of the field of topological data analysis.
• Score: 2.1485350418225244
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The field of mathematical morphology offers well-studied techniques for image processing. In this work, we view morphological operations through the lens of persistent homology, a tool at the heart of the field of topological data analysis. We demonstrate that morphological operations naturally form a multiparameter filtration and that persistent homology can then be used to extract information about both topology and geometry in the images as well as to automate methods for optimizing the study and rendering of structure in images. For illustration, we apply this framework to analyze noisy binary, grayscale, and color images.

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