Quantization level increase in human face images using multilayer neural network

Quantization level increase in human face images using multilayer neural network

フォーマット:

論文

責任表示:

Nakayama, Kenji ; Kimura, Yoshinori ; Katayama, Hiroshi

言語:

英語

出版情報:

IEEE(Institute of Electrical and Electronics Engineers), 1993-10-01

著者名:

掲載情報:

Proceedings of the International Joint Conference on Neural Networks

巻:

2

開始ページ:

1247

終了ページ:

1250

バージョン:

publisher

概要:

In this paper, quantization level increase in human face images using a multilayer neural network (NN) is investigated. Basically speaking, it is impossible to increase quality without any other information. However, when images are limited … to some category, image restoration could be possible, based on the common properties in this category. The multilayer NN is trained using human face images of 32ﾃ・2 pixels with 8-levels as the input data, and 256-level images as the targets. The standard back-propagation (BP) algorithm is employed. 20, 40 and 100 training data are examined. By increasing the training data, a general function of regenerating missing information can be achieved. The internal structure of the trained NN is analyzed using some special input images. As a result, it has been confirmed that the NN regards the input image as the human face, and extracts features of the face. The input image is transformed using these features and the common properties of the training data, extracted and held on the connection weights, to the human face image. 続きを見る

URL:

http://hdl.handle.net/2297/6789

タイトル・著者・出版者が同じ資料

詳細

主題:	Human faces; Multilayer neural networks; Neural networks
受理日:	1993-10-01

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