2-D Invariant Pattern Recognition Using a Backpropogation Network Improved by Distributed Associative Memory

• Main Authors: Jau-Ling Shih, 石昭玲
• Other Authors: Pau-Choo Chung
• Format: Others
• Language: en_US
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/47410554866730966852

碩士 === 國立成功大學 === 電機工程研究所 === 82 === In this paper, a system included image preprocessing and neural networks is proposed. The various function units of the image processing are used to obtain an invariant image representation in the beginning of the system. The space of the neural networks weights can be reduced by using the reduction of the feature dimension before the preprocessed feature applied to the networks.Then, several kinds of the neural models are proposed for pattern recognition : (1) distributed associative memory (DAM), (2) backpropagation network(BPN), (3)DAM combined with BPN, and(4)BPN with the associative memory as initial weights. In the case of (3), this hierarchical networks consist of two levels of neural networks. In the low level, a DAM receives the output vectors of image preprocessing functions to create a system which recognizes pattern regardless of changes in scale or rotation. The higher level is a two- layers BPN which recives the recalled information from the memorized database of the lower level. This neural networks use a BPN after the DAM can raise the recognition ratio in comparison with a DAM, and be faster than a BPN.In the case of (4), the training of the BPN speeds up much because this neural networks use a associative memory of a DAM as initial weights of the first layer of te BPN. Experiment results show that the system can recognize all the patterns correctly when the percentage of the white noises is under under 20% for the case (3) and (4).