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Proceedings Paper

SVD spectral feature of image processing
Author(s): Deshen Xia; Hua Li; Yong Qiu
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Paper Abstract

Since Golub and Reinsch proposed the singular value decomposition (SVD) algorithm in 1970, SVD first became an effective method to the least square problems. Recently, SVD has been successfully applied in many fields, such as image data compression, feature extraction and so on. This paper discourses on the singular value spectral sequence (SVSS), gives the connotation and application of SVSS in the image processing. We find that SVSS can describe the intrinsic feature of the image: each element of SVSS is the most alike image of the original image in corresponding multidimensional space. Using SVD method, we obtain a set of images corresponding the spectral number. With the increase of spectral number, we can get more details of image, otherwise, the more coarse sketch. It means, the head of SVSS represents the low frequency domain. We use SPOT images as samples and gain some effective SVD spectral features. Finally, we get very good extraction and classification. Compared to the Fourier transform, we think SVD method will be a good method in the field of pattern recognition.

Paper Details

Date Published: 29 October 1996
PDF: 8 pages
Proc. SPIE 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling, (29 October 1996); doi: 10.1117/12.256312
Show Author Affiliations
Deshen Xia, Nanjing Univ. of Science and Technology (China)
Hua Li, Nanjing Univ. of Science and Technology (China)
Yong Qiu, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 2904:
Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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