<title>Object segmentation for helicopter guidance</title>

Abstract

Electro-optical sensors can be used to compute range to objects in the flight path of a helicopter. The range depends on the computation of optical flow/motion at different points in the image. The motion algorithms provide a sparse set of ranges as a function of azimuth and elevation. For guidance and display purposes, these discrete set of ranges need to be grouped into sets which correspond to objects in the real world. The requirements of the guidance problem are different from the range segmentation problem encountered in robotics where the primary concern is to determine the shape of a surface to identify an object. The emphasis in this paper is on using clustering techniques on the range information provided by motion algorithms together with the spatial relation provided by the static image to perform object segmentation. The range values are initially grouped into clusters based on depth. Subsequently, the clusters are modified by using the k-means algorithm in the inertial horizontal plane and the minimum spanning tree algorithms in the image plane.