Version 1
: Received: 15 July 2024 / Approved: 16 July 2024 / Online: 16 July 2024 (08:54:23 CEST)
How to cite:
Hrytsyk, V.; Borkivskyi, A.; Oliinyk, T. The Best Symmetry by Finding the Optimal Clustering Filters for Specific Lighting Conditions. Preprints2024, 2024071309. https://doi.org/10.20944/preprints202407.1309.v1
Hrytsyk, V.; Borkivskyi, A.; Oliinyk, T. The Best Symmetry by Finding the Optimal Clustering Filters for Specific Lighting Conditions. Preprints 2024, 2024071309. https://doi.org/10.20944/preprints202407.1309.v1
Hrytsyk, V.; Borkivskyi, A.; Oliinyk, T. The Best Symmetry by Finding the Optimal Clustering Filters for Specific Lighting Conditions. Preprints2024, 2024071309. https://doi.org/10.20944/preprints202407.1309.v1
APA Style
Hrytsyk, V., Borkivskyi, A., & Oliinyk, T. (2024). The Best Symmetry by Finding the Optimal Clustering Filters for Specific Lighting Conditions. Preprints. https://doi.org/10.20944/preprints202407.1309.v1
Chicago/Turabian Style
Hrytsyk, V., Anton Borkivskyi and Taras Oliinyk. 2024 "The Best Symmetry by Finding the Optimal Clustering Filters for Specific Lighting Conditions" Preprints. https://doi.org/10.20944/preprints202407.1309.v1
Abstract
This article explores the efficiency of various clustering methods for image segmentation under different luminosity conditions. Image segmentation plays a crucial role in computer vision appli-cations, and clustering algorithms are commonly used for this purpose. The search for an adaptive clustering mechanism aims to ensure maximum symmetry of real objects with objects/segments in their digital representations. However, clustering methods performance can fluctuate with varying lighting conditions during image capture. Therefore, we assess the performance of several clustering algorithms — including k-means, K-Medoids, fuzzy c-means, Possibilistic C-Means, Gus-tafson-Kessel, Entropy-based Fuzzy, Riddler-Calvard, Kohonen Self-Organizing Maps, and Meanshift — across images captured under different illumination conditions. Additionally, we develop an adaptive image segmentation system utilizing empirical data. Conducted experiments highlight varied performance among clustering methods under different luminosity conditions. This research enhance better understanding of luminosity's impact on image segmentation and aiding in method selection for diverse lighting scenarios.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
