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DC Field | Value | Language |
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dc.contributor.author | Lei, T | - |
dc.contributor.author | Jia, X | - |
dc.contributor.author | Xue, D | - |
dc.contributor.author | Wang, Q | - |
dc.contributor.author | Meng, H | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2021-07-29T18:35:33Z | - |
dc.date.available | 2021-07-29T18:35:33Z | - |
dc.date.issued | 2021-07-26 | - |
dc.identifier | ORCID iDs: Tao Lei https://orcid.org/0000-0002-2104-9298; Xiaohong Jia https://orcid.org/0000-0002-4853-4779; Qi Wang https://orcid.org/0000-0002-7028-4956; Hongying Meng https://orcid.org/0000-0002-8836-1382; Asoke K. Nandi https://orcid.org/0000-0001-6248-2875. | - |
dc.identifier.citation | Lei, T. et al. (2021) 'Fuzzy Students T-Distribution Model Based on Richer Spatial Combination,' IEEE Transactions on Fuzzy Systems, 30 (8), pp. 3023 - 3037. doi: 10.1109/TFUZZ.2021.3099560. | en_US |
dc.identifier.issn | 1063-6706 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23022 | - |
dc.description.abstract | Copyright © The Author(s) 2021. Fuzzy c-means (FCM) algorithms with spatial information have been widely applied in the field of image segmentation. However, most of them suffer from two challenges. One is that introduction of fixed or adaptive single neighboring information with narrow receptive field limits contextual constraints leading to clutter segmentations. The other is that incorporation of superpixels with wide receptive field enlarges spatial coherency leading to block effects. To address these challenges, we propose fuzzy Students t-distribution model based on richer spatial combination (FRSC) for image segmentation. In this Paper, we make two significant contributions. The first is that both narrow and wide receptive fields are integrated into the objective function of FRSC, which is convenient to mine image features and distinguish local difference. The second is that the rich spatial combination under Students t-distribution ensures that spatial information is introduced into the updated parameters of FRSC,which is helpful in finding a balance between the noise-immunity and detail-preservation. Experimental results on synthetic and publicly available images, further demonstrate that the proposed FRSC addresses successfully the limitations of FCM algorithms with spatial information and provides better segmentation results than state-of-the-art clustering algorithms. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China (Grant Number: Grant 61871259, Grant 61861024); Natural Science Basic Research Program of Shaanxi (Grant Number: 2021JC-47); Key Research and Development Program of Shaanxi (Grant Number: 2021ZDLGY08-07). | - |
dc.format.extent | 3023 - 3037 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © The Author(s) 2021. Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | fuzzy c-means (FCM) | en_US |
dc.subject | image segmentation | en_US |
dc.subject | student's t-distribution | en_US |
dc.subject | rich spatial information | en_US |
dc.title | Fuzzy Student’s T-Distribution Model Based on Richer Spatial Combination | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TFUZZ.2021.3099560 | - |
dc.relation.isPartOf | IEEE Transactions on Fuzzy Systems | - |
pubs.issue | 8 | - |
pubs.publication-status | Published | - |
pubs.volume | 30 | - |
dc.identifier.eissn | 1941-0034 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © The Author(s) 2021. Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | 6.04 MB | Adobe PDF | View/Open |
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