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Proceedings Paper

Biological models for automatic target detection
Author(s): Bruce Schachter
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Paper Abstract

Humans are better at detecting targets in literal imagery than any known algorithm. Recent advances in modeling visual processes have resulted from f-MRI brain imaging with humans and the use of more invasive techniques with monkeys. There are four startling new discoveries. 1) The visual cortex does not simply process an incoming image. It constructs a physics based model of the image. 2) Coarse category classification and range-to-target are estimated quickly - possibly through the dorsal pathway of the visual cortex, combining rapid coarse processing of image data with expectations and goals. This data is then fed back to lower levels to resize the target and enhance the recognition process feeding forward through the ventral pathway. 3) Giant photosensitive retinal ganglion cells provide data for maintaining circadian rhythm (time-of-day) and modeling the physics of the light source. 4) Five filter types implemented by the neurons of the primary visual cortex have been determined. A computer model for automatic target detection has been developed based upon these recent discoveries. It uses an artificial neural network architecture with multiple feed-forward and feedback paths. Our implementation's efficiency derives from the observation that any 2-D filter kernel can be approximated by a sum of 2-D box functions. And, a 2-D box function easily decomposes into two 1-D box functions. Further efficiency is obtained by decomposing the largest neural filter into a high pass filter and a more sparsely sampled low pass filter.

Paper Details

Date Published: 14 April 2008
PDF: 21 pages
Proc. SPIE 6967, Automatic Target Recognition XVIII, 69670Y (14 April 2008); doi: 10.1117/12.778496
Show Author Affiliations
Bruce Schachter, Northrop Grumman Corp. (United States)


Published in SPIE Proceedings Vol. 6967:
Automatic Target Recognition XVIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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