四元共空间特征提取算法及其在纸币识别中的应用

doi:10.11959/j.issn.1000-436x.2018285

Abstract

Abstract:

New feature extraction algorithm was proposed based on quaternion common spatial pattern in order to solve the lack of effective description of phase information in the banknote feature extraction and analysis. Firstly, the quaternion matrix was utilized to describe the phase information of the banknote image, and made diagonalization of quaternion composite Hermitian matrix. Secondly, the sample vector was input to the composite quaternion filter. The extracted feature vector was obtained by using the variance of the real part and imaginary part. Finally, the neural network was applied as classifier and the reject class was introduced in the banknote recognition. The experimental results illustrate that the proposed algorithm obtains high recognition rate and meets the real-time requirement of the banknote recognition system. The proposed algorithm has already been applied in a resource-constrained embedded system at the same time.

Key words: quaternion matrix, common spatial pattern, feature extraction, neural network, banknote recognition

CLC Number:

TN92

Shan GAI. Feature extraction algorithm based on quaternion common spatial pattern for banknote recognition[J]. Journal on Communications, 2018, 39(12): 40-46.

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References 12

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人民币(面值/元)	DWT算法+BP神经网络	MASK算法+BP神经网络	GRID算法+BP神经网络	CFFE算法+BP神经网络	QCSP算法+BP神经网络
100	78.56%/2.97%	86.24%/2.08	94.57%/1.64%	97.24%/1.26%	98.75%/0.95%
50	82.49%/1.65%	88.17%/1.27%	92.56%/0.92%	98.04%/0.83%	99.15%/0.74%
20	80.57%/2.47%	89.24%/2.28%	93.17%/1.75%	97.06%/1.19%	98.85%/0.97%
10	85.24%/3.06%	89.07%/2.72%	93.26%/2.18%	98.18%/1.65%	99.24%/1.08%
5	88.56%/2.04%	92.17%/1.62%	95.64%/1.18%	98.84%/0.86%	99.49%/0.72%

美元(USD)	DWT算法+BP神经网络	MASK算法+BP神经网络	GRID算法+BP神经网络	CFFE算法+BP神经网络	QCSP算法+BP神经网络
100	68.29%/2.24%	74.15%/2.07%	80.26%/1.82%	86.54%/1.37%	93.27%/0.86%
50	72.13%/2.95%	78.54%/2.53%	89.25%/1.62%	95.64%/1.08%	98.32%/0.75%
20	80.06%/3.05%	86.49%/2.84%	92.16%/2.16%	97.45%/1.75%	99.05%/1.08%
10	83.14%/1.96%	87.63%/1.37%	94.28%/1.05%	96.57%/0.84%	98.45%/0.57%
2	76.29%/2.05%	79.57%/1.84%	82.15%/1.49%	88.47%/0.97%	94.52%/0.78%
1	80.06%/1.82%	86.27%/1.37%	90.29%/0.92%	95.78%/0.83%	98.42%/0.47%

欧元(EUD)	DWT算法+BP神经网络	MASK算法+BP神经网络	GRID算法+BP神经网络	CFFE算法+BP神经网络	QCSP算法+BP神经网络
500	71.28%/2.45%	75.19%/2.07%	78.24%/1.68%	84.59%/1.05%	89.27%/0.87%
200	80.45%/1.92%	84.27%/1.47%	88.19%/0.95%	92.57%/0.74%	94.85%/0.53%
100	86.94%/2.17%	88.27%/2.05%	94.19%/1.72%	97.28%/1.38%	99.05%/0.96%
50	78.25%/1.82%	84.17%/1.73%	89.26%/1.29%	92.87%/1.05%	95.46%/0.84%
20	68.29%/2.95%	73.84%/2.62%	78.75%/1.84%	86.27%/1.49%	92.19%/0.98%
10	81.57%/1.34%	84.89%/1.18%	88.27%/0.87%	93.59%/0.64%	97.42%/0.45%
5	79.06%/1.45%	86.27%/1.19%	90.15%/0.86%	95.72%/0.73%	98.85%/0.62%

纸币数据库	DWT算法+BP神经网络	MASK算法+BP神经网络	GRID算法+BP神经网络	CFFE算法+BP神经网络	QCSP算法+BP神经网络
D1	96.28	35.74	27.31	25.19	18.64
D2	124.38	53.87	39.27	30.42	25.16
D3	78.84	53.69	28.42	22.37	19.82

Feature extraction algorithm based on quaternion common spatial pattern for banknote recognition

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