Title: De-noising and recovering images based on Kernel PCA theory
Authors: Xi, Pengcheng
Xu, Tao
Citation: WSCG '2004: Posters: The 12-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, 2.-6. February 2004, Plzen, p. 197-200.
Issue Date: 2004
Publisher: UNION Agency
Document type: konferenční příspěvek
URI: http://wscg.zcu.cz/wscg2004/Papers_2004_Poster/J07.pdf
ISBN: 80-903100-6-0
Keywords: kernelová analýza hlavních komponent
Keywords in different language: kernel principal component analysis
Abstract: Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis are called principal components. Since Kernel PCA is just a PCA in feature space F, the projection of an image in input space can be reconstructed from its principal components in feature space. This enables us to perform several applications concerning de-noising and recovering images. Because of the superiority of Kernel PCA over linear PCA, we also get satisfactory effects of de-noising images using Kernel PCA.
Rights: © UNION Agency
Appears in Collections:WSCG '2004: Posters

Files in This Item:
File Description SizeFormat 
J07.pdf257,18 kBAdobe PDFView/Open

Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/974

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.