On the implication of the signal-image curvature: the classic-curvature interpolation functions Online publication date: Mon, 03-Nov-2014
by Carlo Ciulla
International Journal of Machine Intelligence and Sensory Signal Processing (IJMISSP), Vol. 1, No. 2, 2014
Abstract: This work highlights the practical implications of the concept of signal-image curvature. Given a signal-image in the form of a sequel of discrete intensity values and a model interpolation function which has the property of second order differentiability, the curvature is defined as the sum of all of the second order derivatives of the Hessian of the model interpolation function. The geometrical meaning of the curvature is that one of the arctangent of the angle formed by the tangent-line to the first order derivative of the modelled signal data. The novel interpolation function is called classic-curvature interpolation function, and it has the property of improved approximation, versus the model interpolation function, which is manifest in terms of reduced mean absolute error. Additionally, the classic-curvature interpolation functions embed deterministically the math form of the transfer function inherent to the convolution of the model interpolation function.
Online publication date: Mon, 03-Nov-2014
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