Image Multiresolution Discriminant Analysis for Vision-Guided Stability of Micro Air Vehicles

Recently, we have successfully implemented and tested a vision based horizon-tracking algorithm for flight stability and autonomy in Micro Air Vehicles (MAVs). Occasionally, this algorithm fails in scenarios where the underlying Gaussian assumption for the sky and ground appearances is not appropriate. To improve its performance, especially in the presence of video noise, we consider a novel image analysis tool–namely, multiresolution linear discriminant analysis (MLDA) that efficiently detects and economically represents edges in images. The MLDA framework comprises the following components: the MLDA atom, dictionary, tree, graph, and MLDA-based algorithms. In this paper, we explain these components and demonstrate the powerful expressiveness of MLDA, which gives rise to fast geometrical-structure-analysis algorithms. With this approach, not only do we improve sky/ground segmentation results, but also enhance MAV’s potentials for surveillance and monitoring tasks.