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1 September 1990 Optical neural network system for pose determination of spinning satellites
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An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning sateffites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time and hence the paths of object (sateffite) parts. The path traced out by a given part or region is approximately elliptical in image space and the position shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite and the effiptical path of a part in image space the 3-D pose of the satellite is determined. Digital simulation results using this algorithm are presented for various sateffite poses and lighting conditions. 1
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrew John Lee and David P. Casasent "Optical neural network system for pose determination of spinning satellites", Proc. SPIE 1297, Hybrid Image and Signal Processing II, (1 September 1990);

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