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Proceedings Paper

Weighted MLS-SVM for approximation of directional derivatives
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Paper Abstract

Based on statistical learning theory, support vector machine (SVM) is a novel type of learning machine, and it contains polynomial, neural network and radial basis function (RBF) as special cases. The mapped least squares support vector machine (MLS-SVM) is a special least square SVM (LS-SVM), which extends the application of the SVM to the image processing. Based on the MLS-SVM, a family of filters for the approximation of partial derivatives of the digital image surface is designed. Prior information (e.g., local dominant orientation) are incorporated in a two dimension weighted function. The weighted MLS-SVM with the radial basis function kernel is applied to design the proposed filters. Exemplary application of the proposed filters to fingerprint image segmentation is also presented.

Paper Details

Date Published: 3 November 2005
PDF: 7 pages
Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 604417 (3 November 2005); doi: 10.1117/12.655103
Show Author Affiliations
Sheng Zheng, China Three Gorges Univ. (China)
Huazhong Univ. of Science and Technology (China)
Jin Wen Tian, Huazhong Univ. of Science and Technology (China)
Jian Liu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6044:
MIPPR 2005: Image Analysis Techniques
Deren Li; Hongchao Ma, Editor(s)

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