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Proceedings Paper

Automatic Target Detection On The Connection Machine
Author(s): R.Michael Hord
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Paper Abstract

An algorithm to perform automatic target detection has been implemented on the 16K processor Connection Machine at the Perkin-Elmer Advanced Development Center in Oakton, VA. The algorithm accepts as input a single black and white image together with the designation of a few training points from each of two categories termed interesting and uninteresting or target and background. Typically, the input image is an aerial view of vehicles on the ground with 64K pixels. The algorithm computes a five element feature vector at each pixel, and performs two-category classification at the first stage. The features employed are gray level, constant false alarm rate (CFAR) annulus sum, local average, Sobel edge operator, and the MAX-MIN texture measure. The classification process uses a Euclidean distance measure in five dimensional feature space. The second stage of processing uses a connected component algorithm to collect the interesting points into blobs. These blobs are then manipulated to eliminate isolated points. In the third and final stage, blob mensuration is performed to rule out blobs that are too large or too small. The algorithm executes on the CM 500 times faster than on a VAX 11/780.

Paper Details

Date Published: 19 February 1988
PDF: 4 pages
Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); doi: 10.1117/12.942742
Show Author Affiliations
R.Michael Hord, MRJ, Inc. (United States)


Published in SPIE Proceedings Vol. 0848:
Intelligent Robots and Computer Vision VI
David P. Casasent; Ernest L. Hall, Editor(s)

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