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表12 结果

图6 含噪声的测试图像图7噪声滤除后的测试图像

图8 利用ANN训练后的图像图9 测试图像

图10 含噪声的测试图像图11噪声滤除后的测试图像 第五章总结

已经观察到,如果平均误差小于45%,人工神经网络可以用于训练检测从而进行识别。因此,测试图像和原始图像是可识别和成功匹配的。同时也被察到,如果平均误差大于45%,那么图像被识别为不同的图像。在本文中,插入椒盐噪声主要是因为所有用来识别的图像都可能含有某

种噪声,这就需要去滤除噪声,从而进行正确的识别。本文还观察到,由于用来训练的矩阵行数只有原始图像列数的四分之一,用人工神经网络进行训练和测试需要更少的时间。

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