Weighted MLS-SVM for approximation of directional derivatives

Sheng Zheng; Jin Wen Tian; Jian Liu

doi:10.1117/12.655103

3 November 2005 Weighted MLS-SVM for approximation of directional derivatives

Sheng Zheng, Jin Wen Tian, Jian Liu

Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 604417 (2005) https://doi.org/10.1117/12.655103
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

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.

Citation Download Citation

Sheng Zheng, Jin Wen Tian, and Jian Liu "Weighted MLS-SVM for approximation of directional derivatives", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 604417 (3 November 2005); https://doi.org/10.1117/12.655103

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