Classical Cubic spline interpolation needs to solve a set of equations of high dimension. In this work we show how to
compute the interpolant using a FIR digital filter, with a reduced number of operations per interpolated point and high accuracy.
Additionally, the computation can be made on real time as the signal samples are acquired. Following this approach, we show
how to obtain easily the derivatives of the interpolant in a similar way, and also signal approximations to reduce the
oscillations that appear when using high order splines. These techniques are very well suited to compute continuous
representations of image contours on closed shapes and to find its curvature and singularities.Peer ReviewedPostprint (published version
