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Proceedings Paper

Comparison of linear and morphological shared-weight neural networks
Author(s): Yonggwan Won; Paul D. Gader
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Paper Abstract

A hybrid neural network that can learn nonlinear morphological feature extraction and classification simultaneously, called the morphological shared-weight network (MSNN), is described. The feature extraction operation is performed by a gray scale hit-miss transform. The network learns morphological structuring elements by a back-propagation type learning rule. It provides a general problem-independent methodology for designing morphological structuring elements for pattern recognition. The network was applied to handwritten digit recognition and automatic target recognition (ATR) of occluded vehicles and compared to the standard shared-weight neural networks (SSNN) that perform linear feature extraction. For binary handwritten digit recognition, it produced performance comparable to that obtained using existing shared-weight networks. However, it trained faster. For ATR, a set of parking lot images containing a certain type of vehicle was used. An MSNN was trained with non- occluded training vehicles and tested with images containing the training vehicles at various degrees of occlusion. An efficient training method to improve background rejection is introduced. Two target-aim-point selection methods are defined. The MSNN performed significantly better than the SSNN at detecting occluded vehicles and reducing false alarm rates. Furthermore, the morphological network trained significantly faster.

Paper Details

Date Published: 25 March 1996
PDF: 12 pages
Proc. SPIE 2662, Nonlinear Image Processing VII, (25 March 1996); doi: 10.1117/12.235821
Show Author Affiliations
Yonggwan Won, Electronics and Telecommunications Research Institute (United States)
Paul D. Gader, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 2662:
Nonlinear Image Processing VII
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham, Editor(s)

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