The Study And Application Of Key Technology On Multimode Banknote Image Analysis

Banknote is a country business card, the clean degree of banknote in circulation reflects civilization degree and can embody the strength and status of country. The banknote image includes multi-class and multi-mode information such as visible image information, infrared image information, ultraviolet image information, magnetic image information and paper thickness information. Banknote classification and detection of counterfeit currency, defected currency, worn currency and unease circulation currency are completed by analysis and understanding of banknote high reliably. Doing that in order to make circulation safty, reliability and netness. Several key techniques of banknote image analysis are studyied in this paper, and construct a practical banknote sorting system. The really research content is as follows:1. According to the defects of feature extraction methods which has low stability of mask and difficulty discrimination of grid feature, a new banknote feature extraction method based on Haar wavelet transform and fuzzy logic is proposed. Firstly, apply the Haar wavelet transform to the banknote image, and then obtain the approximation and detail coefficients. The fuzzy of banknote image is discribed by fuzzy logic. We make two linguistic variables corresponding two coefficients, and the firing strength is calculated by membership function in the fuzzy feature space. Then the banknote image feature vector is obatined by normalizing the firing strength. Finally, the neural network is applied to classify the banknote image. The extracted feature has sensitiveness and robustness. It is well solved the classification inconformity caused by defected image, noise and distoration during the sample by contact image sensor.2. The wavelet transorm has two drawbacks which are non sensitive direction selection and non sparse reprensentation of banknote image. So the new banknote feature extraction method based on Contourlet transform and fuzzy logic is proposed. The rich textual information of banknote image is extracted by decomposing the banknote image into different directions and resolutions. It has good recognition ability to low quality banknote image. Meanwhile the feature extraction method is posed based on statistical characteristics of coefficients which are at different resolution and direction. The Contourlet transoform is used to decompose the banknote image, and then using the mixture gaussian model describes the coefficients distribution, using the EM algorithm to estimate the parameters of the model. The idea of statistical modeling is applied to banknote image classification firstly.3. In order to capture the rich textual information and local shift information of banknote image, the new banknote image classification method based on rotated quaternion wavelet transform is proposed. The new rotated quaternion wavelet transform is constructed by changing the Garbor filter. The rotated quaternion wavelet consists of one magnitude and three phases, the two phase represent local shift information of image ant the other denote the textual information of image. Firstly, apply the rotated quaternion wavelet transform to the banknote image; then calculate the standard deviation and energy. Finally, using the support vector machine to classify the banknote image. The method obtains high recognition rate and satisfy the real-time requirement of banknote sorting system.4. In order to improve the defect detection rate and decrease the effects of tearing and handwriting, a new banknote image defect detection method is proposed based on wavelet transform. The affine transformation and wavelet transformaton are used to image registration. The edge detector is applied to extract the edge information of banknote image. The banknote image defected feature is obtained by calculating the difference of magnitude. Then the whole banknote image is divided into several same rectangular region, and judge the defect banknote image via regional defect feature. The proposed method has strong resistence to gray scale alteration, and obtain high recognition rate and stability.5. A practical banknote sorting system is finished based on research contents described above and make it to real application.