Approximation properties relative to continuous scale space for hybrid discretizations of Gaussian derivative operators

arXiv:2405.05095v1 Announce Type: cross
Abstract: This paper presents an analysis of properties of two hybrid discretization methods for Gaussian derivatives, based on convolutions with either the normalized sampled Gaussian kernel or the integrated Gaussian kernel followed by central differences. The motivation for studying these discretization methods is that in situations when multiple spatial derivatives of different order are needed at the same scale level, they can be computed significantly more efficiently compared to more direct derivative approximations based on explicit …

abstract analysis approximation arxiv continuous cs.cv cs.na derivatives differences hybrid kernel math.na motivation operators paper scale space studying type