基于反向传播神经网络的平移和旋转不变模式识别方法

Y. Onodera, Hisayoshi Watanabe, A. Taguchi, N. Iijima, M. Sone, H. Mitsui
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引用次数: 11

摘要

提出了一种新的基于神经网络的平移旋转不变量模式识别方法。很明显,在不改进网络结构的情况下,通过对原始模式进行简单的预处理就可以实现左右、上下平移或/和旋转不变性。他们使用一个带有反向传播的三层前馈网络来学习和识别。该方法具有网络尺寸相对较小、学习和识别容易等优点。此外,对于字母。b>,该方法实现了100%的识别率
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Translation and rotation-invariant pattern recognition method using neural network with back-propagation
The authors present a new translation and rotation invariant pattern recognition method using a neural network. It is clear that the left-right, up-down translation or/and rotation invariance are achieved by simple preprocessing of the original patterns without improvement of the network structure. They use a three layer feed-forward network with back-propagation for learning and recognition. The proposed method has the following merits: the net size is relative small, learning and recognition is easy. Moreover, a 100 percent recognition rate is realized by the proposed method, for the alphabet.<>
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