Abstract
In order to acquire a kind of stable and efficient feature sequences to identify different shape of seal images in different angles. Pulse Coupled Neural Networks (PCNN) are adopted to extract the energy logarithmic sequences of seal images, the input image is a binary image, different shape of seal images used as input data of PCNN network to acquire their energy logarithmic sequence as the standard sequence. Then the same flows are used to match the logarithmic sequences of images to be recognized with the standard sequences. In addition, angle rotated seal images also be recognized as identified images. Statistical results analyzed are based on Pearson correlation coefficient. The experimental results of different shapes stamp statistics show that using Pearson correlation coefficient and for statistical experiments compared the sequence that obtained more desirable results. Through many seals experiments proved that the result of Pearson correlation coefficient can reach more than 0.99. The energy logarithmic sequence of different shape of seal images can be used as the feature sequences, which is not impact by the seal’s chop angles, and the feature has a certain stability.
(1) Major Research Projects of Hebei North University (ZD201303). (2) Hebei Province Population Health Information Engineering Technology Research Center.
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Liu, N., Ye, Y., Sun, X., Liang, J., Sun, P. (2016). Rotation Invariant Feature Extracting of Seal Images Based on PCNN. In: Hung, J., Yen, N., Li, KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-10-0539-8_53
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DOI: https://doi.org/10.1007/978-981-10-0539-8_53
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