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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/11610
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| タイトル: | Weighted Census Transform for Feature Representation |
| 著者: | Jeong, Sungmoon
Lee, Hosun
Hamdi, Younes El
Chong, Nak Young |
| キーワード: | Weighted Census Transform
Pattern Classification
Feature Representation
Face Recognition |
| 発行日: | 2013-10-30 |
| 出版者: | Institute of Electrical and Electronics Engineers (IEEE) |
| 誌名: | 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) |
| 開始ページ: | 627 |
| 終了ページ: | 628 |
| DOI: | 10.1109/URAI.2013.6677409 |
| 抄録: | This paper presents a new visual feature representation method as a weighted census transform (WCT) based on modified census transform (MCT) and entropy information of training dataset. The proposed feature representation model can offer robustness to represent same visual images such as MCT feature and sensitivity to effectively classify different visual images. In order to enhance the sensitivity of MCT feature, we designed the different weights for each MCT feature as binary code bit by statistical approach with the training dataset. In order to compare the proposed feature with MCT feature, we fixed classification method such as compressive sensing technique for two features. Experimental results shows that proposed WCT features have better classification performance than traditional MCT features for AR face datasets. |
| Rights: | This is the author's version of the work. Copyright (C) 2013 IEEE. 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2013, 627-628. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | http://hdl.handle.net/10119/11610 |
| 資料タイプ: | author |
| 出現コレクション: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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| 19554.pdf | | 126Kb | Adobe PDF | 見る/開く |
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