教師資料查詢 | 類別: 期刊論文 | 教師: 顏淑惠 Yen Shwu-huey (瀏覽個人網頁)

標題:A Block-Based Orthogonal Locality Preserving Projection Method for Face Super-Resolution
學年100
學期1
出版(發表)日期2012/01/01
作品名稱A Block-Based Orthogonal Locality Preserving Projection Method for Face Super-Resolution
作品名稱(其他語言)
著者Yen, Shwu-huey; Wu, Che-ming; Wang, Hung-zhi
單位淡江大學資訊工程學系
出版者Heidelberg: Springer Berlin Heidelberg
著錄名稱、卷期、頁數Lecture Notes in Computer Science 7197, pp.253-262
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.
關鍵字Orthogonal Locality Preserving Projections; OLPP; manifold; super-resolution; General Regression Neural Network; GRNN
語言英文(美國)
ISSN0302-9743
期刊性質國外
收錄於EI
產學合作
通訊作者
審稿制度
國別德國
公開徵稿
出版型式電子版
相關連結
Google+ 推薦功能,讓全世界都能看到您的推薦!