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METHOD AND SYSTEM FOR SPLICING AND RESTORING SHREDDED PAPER BASED ON EXTREME LEARNING MACHINE

机译:基于极端学习机的碎纸拼接与还原方法及系统

摘要

The present invention discloses a method and system for splicing and restoring shredded paper based on an extreme learning machine. The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the training sample; training an extreme learning machine neural network model according to the left and right boundary feature data, to obtain a trained neural network model; acquiring a shredded paper test sample to be spliced; extracting left and right boundary feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting shredded paper with a highest degree of coincidence with the first piece of to-be-spliced shredded paper by the trained neural network model; determining whether the shredded paper with the highest degree of coincidence is correctly spliced to the first piece of to-be-spliced shredded paper; if yes, splicing shredded paper until all the shredded paper is spliced and restored; and if not, adopting manual marking, and continuing to select shredded paper with a highest degree of coincidence with the first piece of to-be-spliced shredded paper by the trained neural network model. The method and system for splicing and restoring shredded paper based on an extreme learning machine can well splice and restore shredded paper quickly.;Disclosed is a method and system for splicing and restoring shredded paper based on an extreme learning machine (“ELM”). The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the sample; training an ELM neural network model according to the feature data to obtain a trained neural network model (“TNNM”); acquiring a shredded paper test sample to be spliced; extracting feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting, by the TNNM, a shredded piece with a highest degree of coincidence with the first piece; determining whether the shredded piece is correctly spliced to the first piece; if yes, splicing shredded paper until all shredded paper is spliced and restored; if not, adopting manual marking, and continuing to select, by the TNNM, shredded paper with a highest degree of coincidence with the first piece.
机译:本发明公开了一种基于极限学习机的拼接和还原碎纸的方法和系统。该方法包括:获取待拼接的切纸训练样本;以及提取训练样本的左右边界特征数据;根据左右边界特征数据训练极限学习机神经网络模型,得到训练后的神经网络模型。取得切碎的纸张测试样本以进行拼接;提取测试样本的左右边界特征数据;选择第一张要拼接的碎纸;通过训练后的神经网络模型选择与第一张要拼接的切碎纸相吻合度最高的切碎纸;确定重合度最高的切碎纸是否正确拼接到第一张要拼接的切碎纸上;如果是,则将切碎的纸拼接起来,直到所有切碎的纸都被拼接并还原为止;如果不是,则采用人工标记,并通过训练后的神经网络模型继续选择与第一张要拼接的碎纸高度吻合的碎纸。一种基于极限学习机的切纸拼接与还原方法和系统,可以很好地快速拼接和还原切碎的纸张。公开了一种基于极限学习机的纸拼接与还原方法与系统。该方法包括:获取待拼接的切纸训练样本;以及提取样本的左右边界特征数据;根据特征数据训练ELM神经网络模型,以获得训练后的神经网络模型(TNNM);取得切碎的纸张测试样本以进行拼接;提取测试样品的特征数据;选择第一张要拼接的碎纸; TNNM选择与第一个片段最重合的切碎片段;确定切碎的碎片是否正确地拼接到第一碎片上;如果是,请对切碎的纸张进行拼接,直到所有切碎的纸张都被拼接并恢复为止;如果不是,则采用手工标记,并由TNNM继续选择与第一张纸最重合的碎纸。

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