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Finally, we obtain the radius function.
A method based on the speed of species is presented for determining the radius function.
First, corner vertices are obtained as minimum radius peaks in the discrete radius function, where radius is obtained from differences.
Second, approximated piecewise parametric curves on the stroke are obtained and the analytic radius function is calculated.
The radius function to obtain curves is calculated from approximated piecewise curves, which is much more noise free than discrete radius calculation.
In this article we present a robust approach based on the approximation to parametric cubic curves of the stroke for further radius function calculation in order to detect corner and tangent vertices.
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In particular, a family of planar curves corresponding to which the curvature radius functions are polynomials in terms of the tangent angle are used for the purpose.
By choosing low order polynomials for representing the curvature radius functions, the interpolating curves can be spirals that have monotone curvatures or fair curves with small numbers of curvature extremes.
Training of the SVM was performed using radius basis function (RBF) kernels function and five-fold cross validation.
Training of the SVM-RFE-CV was performed using radius basis function (RBF) kernels function and leave-one-out cross-validation.
To solve the nonlinear classification problem, we can map first the training data to another feature space F via a nonlinear map φ: ℝn → F and then perform the above computations in F. We used Gaussian radius basis function (RBF) kernels function for SVM.
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