Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Xi, Pengcheng | |
dc.contributor.author | Xu, Tao | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2013-01-25T12:18:54Z | |
dc.date.available | 2013-01-25T12:18:54Z | |
dc.date.issued | 2004 | |
dc.identifier.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. | en |
dc.identifier.isbn | 80-903100-6-0 | |
dc.identifier.uri | http://wscg.zcu.cz/wscg2004/Papers_2004_Poster/J07.pdf | |
dc.identifier.uri | http://hdl.handle.net/11025/974 | |
dc.description.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. | en |
dc.format | 4 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | UNION Agency | en |
dc.relation.ispartofseries | WSCG '2004: Posters | en |
dc.rights | © UNION Agency | cs |
dc.subject | kernelová analýza hlavních komponent | cs |
dc.title | De-noising and recovering images based on Kernel PCA theory | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.subject.translated | kernel principal component analysis | en |
dc.type.status | Peer-reviewed | en |
Appears in Collections: | WSCG '2004: Posters |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/974
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