Presentation | 2004/9/19 Extraction of facial regions from images using Self Organizing Maps Yoshinori KAWASE, Tomoharu NAGAO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This report presents a method to extract facial regions from human images. As one of such methods, a method to categorize each rectangular region in a give image into facial or not facial is known. Template matching method is a typical one, and each rectangular region is compared with a standard template image by calculating a distance between them. The most typical distance is root mean square of differences between gray values of the template image pixels and the corresponding pixels of the rectangular region. In this case, a lot of error detections will occur since two dimensional distribution of pixel values is not considered. Therefore, we propose to use SOM; Self Organizing Maps known as one of the most useful classifying method without teachers. First, we construct an one dimensional map to categorize a rectangular region into facial or not facial through learning. Secondly, we show that the detection method using this map is more useful than template matching through several experimental results. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Facial Regions / SOM / Self Organizing Maps / Template Matching |
Paper # | HCS2004-21 |
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Committee | HCS |
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Conference Date | 2004/9/19(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Human Communication Science (HCS) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Extraction of facial regions from images using Self Organizing Maps |
Sub Title (in English) | |
Keyword(1) | Facial Regions |
Keyword(2) | SOM |
Keyword(3) | Self Organizing Maps |
Keyword(4) | Template Matching |
1st Author's Name | Yoshinori KAWASE |
1st Author's Affiliation | Graduate School of Environment and Information Sciences() |
2nd Author's Name | Tomoharu NAGAO |
2nd Author's Affiliation | Graduate School of Environment and Information Sciences |
Date | 2004/9/19 |
Paper # | HCS2004-21 |
Volume (vol) | vol.104 |
Number (no) | 320 |
Page | pp.pp.- |
#Pages | 6 |
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