Comparison of linear and morphological shared-weight neural networks

Yonggwan Won; Paul D. Gader

doi:10.1117/12.235821

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25 March 1996 Comparison of linear and morphological shared-weight neural networks

Yonggwan Won, Paul D. Gader

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Proceedings Volume 2662, Nonlinear Image Processing VII; (1996) https://doi.org/10.1117/12.235821
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

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.

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Yonggwan Won and Paul D. Gader "Comparison of linear and morphological shared-weight neural networks", Proc. SPIE 2662, Nonlinear Image Processing VII, (25 March 1996); https://doi.org/10.1117/12.235821

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