A fast algorithm for statistically optimized orientation estimation

Filtering a signal with a finite impulse response (FIR) filter introduces dependencies between the errors in the filtered image due to overlapping filter masks. If the filtering only serves as a first step in a more complex estimation problem (e.g. orientation estimation), then these correlations can turn out to impair estimation quality. The aim of this paper is twofold. First, we show that orientation estimation (with estimation of optical flow being an important special case for space-time volumes) is a Total Least Squares (TLS) problem: Tp approximate to 0 with sought parameter vector p and given TLS data matrix T whose statistical properties can be described with a covariance tensor. In the second part, we will show how to improve TLS estimates given this statistical information.