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Proceedings Paper

Region growing and object classification using a neural network
Author(s): Patrick T. Gaughan; Gerald M. Flachs
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Paper Abstract

A neural network architecture is presented to segment and recognize objects of interest. The architecture consists of a region growing net to segment regions of interest by propagating activity through the neural lattice formed by the image pixels using local features as synaptic weights. A supervisory net utilizes the Fourier descriptors of the segmented region to characterize its shape and control the region growing net. The neural net is applied to segment objects of varying clarity to measure its performance and robustness in the presence of cluttered backgrounds and noisy object boundaries. Finally the segmentation and supervisory nets are combined and applied to the practical problem of segmenting roads from aerial photographs. 1.

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21169
Show Author Affiliations
Patrick T. Gaughan, New Mexico State Univ. (United States)
Gerald M. Flachs, New Mexico State Univ. (United States)

Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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