Polygonal Meshes of Highly Noisy Images based on a New Symmetric Thinning Algorithm with Theoretical Guarantees

Siddiqui, Mohammed Arshad and Kurlin, Vitaliy ORCID: 0000-0001-5328-5351 (2020) Polygonal Meshes of Highly Noisy Images based on a New Symmetric Thinning Algorithm with Theoretical Guarantees. In: 15th International Conference on Computer Vision Theory and Applications, 2020-2-27 - 2020-2-29.

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Official URL: http://dx.doi.org/10.5220/0009340301370146

Abstract

Microscopic images of vortex fields are important for understanding phase transitions in superconductors. These optical images include noise with high and variable intensity, hence are manually processed to extract numerical data from underlying meshes. The current thinning and skeletonization algorithms struggle to find connected meshes in these noisy images and often output edge pixels with numerous gaps and superfluous branching point. We have developed a new symmetric thinning algorithms to extract from such highly noisy images 1-pixel wide skeletons with theoretical guarantees. The resulting skeleton is converted into a polygonal mesh that has only polygonal edges at sub-pixel resolution. The experiments on over 100 real and 6250 synthetic images establish the state-of-the-art in extracting optimal meshes from highly noisy images.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Edge Detection, Thinning, Skeletonization, Polygonal Mesh
Depositing User:	Symplectic Admin
Date Deposited:	07 May 2020 10:27
Last Modified:	18 Jan 2023 23:52
DOI:	10.5220/0009340301370146
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3086285