Subpixel-edge detection is the first stage in processing many high-level vision algorithms. However, the study of the statistical properties of such data has remained incomplete in most research. We present a method for estimating the second-order moments of the subpixel-edge position, computed by a deterministic algorithm based on three quadratic interpolations. The algorithm is tested on different types of noise (Gaussian, impulse, colored) and compared with two methods in the literature. The results show that this approach remains accurate even at high noise levels.
The aim of this paper is to analyze the statistical properties of the Harris corner detector. Usually, the noise effect is computed using a linear model of the corner response H. Our approach, is different and propagate the error through the unmodified expression of H. The experimental results compared to Monte-Carlo simulations show the interest of this method.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.