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    • 12. 发明授权
    • Pattern searching method using neural networks and correlation
    • 使用神经网络的模式搜索方法和相关性
    • US5995652A
    • 1999-11-30
    • US910265
    • 1997-08-13
    • Chinchuan ChiuToru OkiPhilip Paolella
    • Chinchuan ChiuToru OkiPhilip Paolella
    • G06K9/32G06K9/64G06K9/62
    • G06K9/6203G06K9/3233
    • A pattern searching method using neural networks and correlation. This method combines the quickness and adaptiveness of neural networks with the accuracy of the mathematical correlation approach. Images are divided into small sub-images which are presented to the trained neural network. Sub-images that may contain the pattern or partial pattern are selected by the neural network. The neural network also provides the approximate location of the pattern, therefore the selected sub-images can be adjusted to contain the complete pattern. Desired patterns can be located by measuring the new sub-images' correlation values against the reference models in a small area. Experiments show that this superior method is able to find the desired patterns. Moreover, this method is much faster than traditional pattern searching methods which use only correlation.
    • 使用神经网络和相关性的模式搜索方法。 该方法将神经网络的快速性和适应性与数学相关方法的准确性相结合。 图像被分为呈现给训练神经网络的小的子图像。 可能包含图案或部分图案的子图像由神经网络选择。 神经网络还提供了图案的大致位置,因此可以调整所选择的子图像以包含完整图案。 可以通过在小区域中针对参考模型测量新的子图像的相关值来定位所需的图案。 实验表明,这种优越的方法能够找到所需的模式。 此外,该方法比仅使用相关性的传统模式搜索方法快得多。
    • 16. 发明授权
    • Pattern searching method using neural networks and correlation
    • 使用神经网络的模式搜索方法和相关性
    • US5696838A
    • 1997-12-09
    • US376544
    • 1995-01-23
    • Chinchuan ChiuToru OkiPhilip Paolella
    • Chinchuan ChiuToru OkiPhilip Paolella
    • G06F15/18G06G7/60G06K9/32G06K9/64G06N3/063G06T7/00G06K9/62
    • G06K9/6203G06K9/3233
    • A pattern searching method using neural networks and correlation. This method combines the quickness and adaptiveness of neural networks with the accuracy of the mathematical correlation approach. Images are divided into small sub-images which are presented to the trained neural network. Sub-images that may contain the pattern or partial pattern are selected by the neural network. The neural network also provides the approximate location of the pattern, therefore the selected sub-images can be adjusted to contain the complete pattern. Desired patterns can be located by measuring the new sub-images' correlation values against the reference models in a small area. Experiments show that this superior method is able to find the desired patterns. Moreover, this method is much faster than traditional pattern searching methods which use only correlation.
    • 使用神经网络和相关性的模式搜索方法。 该方法将神经网络的快速性和适应性与数学相关方法的准确性相结合。 图像被分为呈现给训练神经网络的小的子图像。 可能包含图案或部分图案的子图像由神经网络选择。 神经网络还提供了图案的大致位置,因此可以调整所选择的子图像以包含完整图案。 可以通过在小区域中针对参考模型测量新的子图像的相关值来定位所需的图案。 实验表明,这种优越的方法能够找到所需的模式。 此外,该方法比仅使用相关性的传统模式搜索方法快得多。