This paper is concerned with the development and implementation of a registration and stabilization method in conjunction with airborne imaging applications. We consider the situations for which the camera motion and vibration collectively affect the noisy image sequence. The general routine presented in this work is a combination of two algorithms for global image registration and image stabilization. We use and present experiments with real image sequences to track a moving object in the direction of its motion trajectory.
This paper presents a method for registration of noisy airborne images for the purpose of the detection of moving objects. A new iterative algorithm is developed and presented for the correction of geometrical distortion caused by global motion in a scene. A binary hypotheses test is subsequently established using a likelihood ratio test (LRT) to classify the pixels in the corrected image as either locally moving (object motion) or not moving (stationary). The paper also incorporates the use of the Expectation-Maximization method for estimation of statistical image features needed by the LRT. We use and present experiments with real image sequences to validate the analytical developments.
SUMO/FREND is a risk reduction program for an advanced servicing spacecraft sponsored by DARPA and executed by
the Naval Center for Space Technology at the Naval Research Laboratory in Washington, DC. The overall program will
demonstrate the integration of many techniques needed in order to autonomously rendezvous and capture customer
satellites at geosynchronous orbits. A flight-qualifiable payload is currently under development to prove out challenging
aspects of the mission. The grappling process presents computer vision challenges to properly identify and guide the
final step in joining the pursuer craft to the customer. This paper will provide an overview of the current status of the
project with an emphasis on the challenges, techniques, and directions of the machine vision processes to guide the
grappling.
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