English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 49378/84106 (59%)
造訪人次 : 7372437      線上人數 : 44
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/78692


    題名: A Block-Based Orthogonal Locality Preserving Projection Method for Face Super-Resolution
    作者: Yen, Shwu-huey;Wu, Che-ming;Wang, Hung-zhi
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Orthogonal Locality Preserving Projections;OLPP;manifold;super-resolution;General Regression Neural Network;GRNN
    日期: 2012
    上傳時間: 2012-10-19 17:02:04 (UTC+8)
    出版者: Heidelberg: Springer Berlin Heidelberg
    摘要: Due to cost consideration, the quality of images captured from surveillance systems usually is poor. To restore the super-resolution of face images, this paper proposes to use Orthogonal Locality Preserving Projections (OLPP) to preserve the local structure of the face manifold and General Regression Neural Network (GRNN) to bridge the low-resolution and high-resolution faces. In the system, a face is divided into four blocks (forehead, eyes, nose, and mouth). The super-resolution process is applied on each block then combines them into a complete face. Comparing to existing methods, the proposed method has shown an improved and promising result.
    關聯: Lecture Notes in Computer Science 7197, pp.253-262
    DOI: 10.1007/978-3-642-28490-8_27
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML147檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋