Determination of Stationary Points and Their Bindings in Dataset using RBF Methods
For researchers in computer vision or chemical physics needing to analyze stationary points from sampled surfaces, but the contribution is incremental as it applies known RBF interpolation to a specific problem without demonstrated performance gains.
The paper proposes an algorithm to find stationary points and their geometric bindings (e.g., line segments, circles) from sampled surface data without knowing the underlying function, using piecewise RBF interpolation. No concrete numerical results are provided.
Stationary points of multivariable function which represents some surface have an important role in many application such as computer vision, chemical physics, etc. Nevertheless, the dataset describing the surface for which a sampling function is not known is often given. Therefore, it is necessary to propose an approach for finding the stationary points without knowledge of the sampling function. In this paper, an algorithm for determining a set of stationary points of given sampled surface and detecting the bindings between these stationary points (such as stationary points lie on line segment, circle, etc.) is presented. Our approach is based on the piecewise RBF interpolation of the given dataset.