Presentation | 2012-11-17 Design of probabilistic image inpainting filters using Gaussian graphial models Tomotaka KITAGAWA, Muneki YASUDA, Kazuyuki TANAKA, |
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Abstract(in English) | A probabilistic image inpainting filter is an image processing filter that reconstructs lost or deteriorated pixels of images by using information of other pixels in the images. Pixel-based probabilistic image inpainting filters on the basis of a Gaussian graphical models (Gaussian Markov random fields), which can fast reconstruct damaged parts of images, have been proposed by some authors. In this paper, to reconstruct larger damaged regions in images, we propose a new texture-based probabilistic image inpainting filter by extending the pixel-based probabilistic image inpainting filters. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | image inpainting / Bayes statistics / probabilistic information processing / Gaussian graphical model / maximum likelihood estimation |
Paper # | NC2012-71 |
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Committee | NC |
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Conference Date | 2012/11/9(1days) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Design of probabilistic image inpainting filters using Gaussian graphial models |
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Keyword(1) | image inpainting |
Keyword(2) | Bayes statistics |
Keyword(3) | probabilistic information processing |
Keyword(4) | Gaussian graphical model |
Keyword(5) | maximum likelihood estimation |
1st Author's Name | Tomotaka KITAGAWA |
1st Author's Affiliation | Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University() |
2nd Author's Name | Muneki YASUDA |
2nd Author's Affiliation | Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University |
3rd Author's Name | Kazuyuki TANAKA |
3rd Author's Affiliation | Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University |
Date | 2012-11-17 |
Paper # | NC2012-71 |
Volume (vol) | vol.112 |
Number (no) | 298 |
Page | pp.pp.- |
#Pages | 6 |
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