| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2023-03-07 |
| タイトル |
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タイトル |
A Spatially Correlated Mixture Model for Image Segmentation |
| 言語 |
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言語 |
eng |
| キーワード |
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主題 |
image segmentation |
| キーワード |
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主題 |
Gaussian processes |
| キーワード |
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主題 |
mixture models |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
KURISU, Kosei
SUEMATSU, Nobuo
IWATA, Kazunori
HAYASHI, Akira
栗栖, 昂勢
末松, 伸朗
岩田, 一貴
林, 朗
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
In image segmentation, finite mixture modeling has been widely used. In its simplest form, the spatial correlation among neighboring pixels is not taken into account, and its segmentation results can be largely deteriorated by noise in images. We propose a spatially correlated mixture model in which the mixing proportions of finite mixture models are governed by a set of underlying functions defined on the image space. The spatial correlation among pixels is introduced by putting a Gaussian process prior on the underlying functions. We can set the spatial correlation rather directly and flexibly by choosing the covariance function of the Gaussian process prior. The effectiveness of our model is demonstrated by experiments with synthetic and real images. |
| 書誌情報 |
IEICE Transactions on Information and Systems
巻 E98-D,
号 4,
p. 930-937,
発行日 2015-04-01
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| 出版者 |
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出版者 |
電子情報通信学会 |
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
09168532 |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA10826272|AA11226532 |
| 論文ID（NAID） |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
NAID |
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関連識別子 |
130005061856 |
| DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
info:doi/https://doi.org/10.1587/transinf.2014EDP7307 |
| 権利 |
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|
権利情報 |
Copyright © 2015 The Institute of Electronics, Information and Communication Engineers |
| 関連サイト |
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識別子タイプ |
URI |
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関連識別子 |
http://www.ieice.org/jpn/trans_online/index.html |
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関連名称 |
http://www.ieice.org/jpn/trans_online/index.html |
| フォーマット |
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内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
| 著者版フラグ |
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出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
e98-d_4_930.pdf (946.0 kB)
