Spatial \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}-Trimmed Fuzzy C-Means Algorithm to Image Segmentation
| Autor: | Vela-Rincón, Virna V., Mújica-Vargas, Dante, Mejía Lavalle, Manuel, Magadán Salazar, Andrea |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Image segmentation
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}-Trimmed means filter Fuzzy C-Means Spatial information Article |
| Zdroj: | Pattern Recognition |
| Popis: | An important aspect should be taken into account, when an image is segmented, the presence of atypical information. In this investigation an algorithm is proposed that is noise tolerant in the segmentation process. A method to image segmentation that combines Fuzzy C-Means (FCM) algorithm and Trimmed Means filter, called Spatial \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} Trimmed Fuzzy C-means, using local information to achieve better segmentation. The FCM is very sensitive to noise, and the Trimmed Means filter is used to eliminate outliers with a lower computational cost. Compared to some state-of-the-art algorithms, the proposed is faster and noise tolerant, demonstrating better performance in the metrics considered. |
| Databáze: | OpenAIRE |
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