Image Pattern Recognition in Natural Environment Using Morphological Feature Extraction

Authors:
Yonggwan Won;Jiseung Nam;Bae-Ho Lee
Affiliations:
-;-;-
Venue:
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Year:
2000

Citing 7
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Abstract

The gray-scale morphological Hit-or-Miss transform is theoretically invariant to vertical translation of the input function, which is analogous to gray-value shift of the input images. Designing optimal structuring elements for the Hit-or-Miss transform operator is achieved by neural network learning methodology using a shared-weight neural network (SWNN) architecture. Early stage of the neural network system performs feature extraction using the operator, while the late stage does classification. In experimental studies, this morphological feature-based neural network (MFNN) system is applied to location of human face and automatic recognition of vehicle license plate to examine the property of the operator. The results of the experimental studies show that the gray-scale morphological Hit-or-Miss transform operator is reducing the effects of lighting variation.