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Proceedings Paper

Multisensor object segmentation using a neural network
Author(s): Patrick T. Gaughan; Gerald M. Flachs; Jay B. Jordan
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Paper Abstract

A neural network architecture is presented to segment objects using multiple sensor/feature images. The neural architecture consists of a region growing net to separate an object from the surrounding background based upon local statistical properties. The region growing net consists of a lattice of neural processing elements for propagating a similarity activity between image pixels. A potential function approach is presented to define the neural weights by measuring pixel similarity in multisensor/feature images. The performance of the neural segmenter is demonstrated by comparing its performance to that of an architecture using a statistical decision theoretic technique.

Paper Details

Date Published: 1 August 1991
PDF: 8 pages
Proc. SPIE 1469, Applications of Artificial Neural Networks II, (1 August 1991); doi: 10.1117/12.45019
Show Author Affiliations
Patrick T. Gaughan, New Mexico State Univ. (United States)
Gerald M. Flachs, New Mexico State Univ. (United States)
Jay B. Jordan, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 1469:
Applications of Artificial Neural Networks II
Steven K. Rogers, Editor(s)

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