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Efficient Fuzzy C-Means Architecture for Image Segmentation
This paper presents a novel VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the membership matrix and cluster centroid...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231657/ https://www.ncbi.nlm.nih.gov/pubmed/22163980 http://dx.doi.org/10.3390/s110706697 |
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author | Li, Hui-Ya Hwang, Wen-Jyi Chang, Chia-Yen |
author_facet | Li, Hui-Ya Hwang, Wen-Jyi Chang, Chia-Yen |
author_sort | Li, Hui-Ya |
collection | PubMed |
description | This paper presents a novel VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. In addition, an efficient pipelined circuit is used for the updating process for accelerating the computational speed. Experimental results show that the the proposed circuit is an effective alternative for real-time image segmentation with low area cost and low misclassification rate. |
format | Online Article Text |
id | pubmed-3231657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32316572011-12-07 Efficient Fuzzy C-Means Architecture for Image Segmentation Li, Hui-Ya Hwang, Wen-Jyi Chang, Chia-Yen Sensors (Basel) Article This paper presents a novel VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. In addition, an efficient pipelined circuit is used for the updating process for accelerating the computational speed. Experimental results show that the the proposed circuit is an effective alternative for real-time image segmentation with low area cost and low misclassification rate. Molecular Diversity Preservation International (MDPI) 2011-06-27 /pmc/articles/PMC3231657/ /pubmed/22163980 http://dx.doi.org/10.3390/s110706697 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Li, Hui-Ya Hwang, Wen-Jyi Chang, Chia-Yen Efficient Fuzzy C-Means Architecture for Image Segmentation |
title | Efficient Fuzzy C-Means Architecture for Image Segmentation |
title_full | Efficient Fuzzy C-Means Architecture for Image Segmentation |
title_fullStr | Efficient Fuzzy C-Means Architecture for Image Segmentation |
title_full_unstemmed | Efficient Fuzzy C-Means Architecture for Image Segmentation |
title_short | Efficient Fuzzy C-Means Architecture for Image Segmentation |
title_sort | efficient fuzzy c-means architecture for image segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231657/ https://www.ncbi.nlm.nih.gov/pubmed/22163980 http://dx.doi.org/10.3390/s110706697 |
work_keys_str_mv | AT lihuiya efficientfuzzycmeansarchitectureforimagesegmentation AT hwangwenjyi efficientfuzzycmeansarchitectureforimagesegmentation AT changchiayen efficientfuzzycmeansarchitectureforimagesegmentation |