Horizon extraction in an optical collision avoidance sensor

Extraction and utilization of the horizon contour is presented within the context of an optical collision avoidance instrument, comprised of individual nodes configured in a fixed topology distributed network. The algorithm iterates between learning and application stages. Pixel neighbourhoods were classified into ground and sky regions using a vector of feature descriptors. The clusters in feature space were separated by a learnt minimal-error threshold, which was subsequently applied to the entire image, or a pre-selected region of-interest aided by scenario-dependent constraints. Morpho logical operations reduced spurious clusters. The resultant contour was parametrically fitted to a polynomial for comparison to ground-truth. Adaptive operation allows inputs from prior measurements and external attitude information.