Hierarchical neural architecture for visual pattern recognition and reconstruction

Jagath C. Rajapakse; Raj S. Acharya

doi:10.1117/12.19588

1 July 1990 Hierarchical neural architecture for visual pattern recognition and reconstruction

Jagath C. Rajapakse, Raj S. Acharya

Proceedings Volume 1246, Parallel Architectures for Image Processing; (1990) https://doi.org/10.1117/12.19588
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States

Abstract

A hierarchical self-organizing neural network which can recognize and reconstruct the traces of the previously learned binary patterns is presented. The recognition and reconstruction properties of the network are invariant with respect to distortion, noise, translation, scaling and partial rotation of the original training patterns. If two or more patterns are presented simultaneously, the network pays attention to each pattern selectively. The network can incorporate new training patterns for recognition without loosing its previously learned information. We demonstrate the usefulness of the network in image recognition, reconstruction and segmentation with simulation results.

Citation Download Citation

Jagath C. Rajapakse and Raj S. Acharya "Hierarchical neural architecture for visual pattern recognition and reconstruction", Proc. SPIE 1246, Parallel Architectures for Image Processing, (1 July 1990); https://doi.org/10.1117/12.19588

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