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4 April 1997 Detection and orientation classifier for the VIGILANTE image processing system
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Abstract
VIGILANTE is an automated recognition and tracking system that closely integrates a sensing platform with a very large processing capability (over 2 TeraOPS). The architecture currently consists of an optical bench with multiple sensors, a large parallel analog pre-processor, and a digital 512 processor, parallel machine. Preliminary results on target detection and orientation are presented for an algorithm that is suitable for the VIGILANTE architecture. The technique makes use of eigenvectors calculated from image blocks (size 32 X 32) drawn from video sequences containing rocket targets. The eigenvectors are used to reduce the dimensionality of frame-lets (size 32 X 32) from the larger sensor images. These frame-lets are projected on to the eigenvectors and the resultant values are then used as an input pattern to a feed forward neural network classifier. A description and evaluation of this algorithm (including precision limitation) with respect to VIGILANTE is provided. Experiments using this technique have generated near 99target and non-target images and close to 97% identification of the rocket type.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Curtis W. Padgett, Michael F. Zhu, and Steven C. Suddarth "Detection and orientation classifier for the VIGILANTE image processing system", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271479
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