Presentation | 1993/5/26 Quantization Level increase in Human Face Using Multilayer Neural Network Yoshinori Kimura, Hiroshi Katayama, Kenji Nakayama, |
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Abstract(in English) | Quantization level increase in human face images using multilayer neural network(NN)is discussed.Basically speaking,it is impossible to increase quantization level without any other information.In this paper,however,images are limited to human faces.Therefore,a point of this discussion is the following:Is it possible to extract genera feature of human face by using NN? In order to investigate this point,the multilayer NN is trained using 8-level images as inputs,and the same images with 256-levels as targets.Back-propagation algorithm is employed.By using about 100 face images,the NN can transform untrained 8-level face images to 256-level face images.The trained NN holds facility of transforming arbitrary input images to human face like images. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Multilayer Neural Network / Face Image / Quantization Level / Image Restoration / Image Transformation |
Paper # | NC93-2 |
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Committee | NC |
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Conference Date | 1993/5/26(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) | Quantization Level increase in Human Face Using Multilayer Neural Network |
Sub Title (in English) | |
Keyword(1) | Multilayer Neural Network |
Keyword(2) | Face Image |
Keyword(3) | Quantization Level |
Keyword(4) | Image Restoration |
Keyword(5) | Image Transformation |
1st Author's Name | Yoshinori Kimura |
1st Author's Affiliation | Department of Electrical and Computer Engineering,Faculty of Technology,Kanazawa University() |
2nd Author's Name | Hiroshi Katayama |
2nd Author's Affiliation | Fujitsu Limited |
3rd Author's Name | Kenji Nakayama |
3rd Author's Affiliation | Department of Electrical and Computer Engineering,Faculty of Technology,Kanazawa University |
Date | 1993/5/26 |
Paper # | NC93-2 |
Volume (vol) | vol.93 |
Number (no) | 67 |
Page | pp.pp.- |
#Pages | 7 |
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