Principal component analysis for feature extraction of image sequence

This paper presents a method to extract features by PCA (Principal component analysis) from a series of woods surfaces' images. The method introduces a principal component subspace and can reserve original information while extraction mainly information. The emulated results show that we can fu...

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Bibliographic Details
Published in2010 International Conference on Computer and Communication Technologies in Agriculture Engineering Vol. 1; pp. 250 - 253
Main Author Binjie Xiao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Artificial neural networks
defects detection
Eigenvalues and eigenfunctions
features extraction
image series
Principal component analysis
Principal component analysis component
woods surface inspection
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Summary:This paper presents a method to extract features by PCA (Principal component analysis) from a series of woods surfaces' images. The method introduces a principal component subspace and can reserve original information while extraction mainly information. The emulated results show that we can fuse the image series and extract features from the four images of a same surface by using this method. After processing the sequences of images, we get a feature which is good for the next classification and recognition process.
ISBN:1424469449
9781424469444
ISSN:2161-1092
DOI:10.1109/CCTAE.2010.5544358