基于多角度混合高斯模型的纸币图像识别算法

Banknote image recognition algorithm based on multi-angle gaussian mixture model

针对金融领域的应用需求,提出了一种基于双面网格特征和多角度混合高斯模型的纸币图像识别算法。该算法使用双面网格作为特征提取方法,并根据类内、类间距离求取网格划分数量,证明双面网格特征相较单面网格特征的优势。然后针对不同倾斜角训练多角度混合高斯模型作为分类器,省去对整幅图像进行旋转的预处理过程,简化识别流程。改进的高斯密度判决函数进一步降低了时间消耗。实验结果表明,该算法的识别率可以达到100%,识别速度4ms/张,准确性和实时性都得到了很好的保证。

Aiming at the application requirements of financial sector,a banknote image recognition algorithm based on double-sided mesh feature and multi-angle Gaussian mixture model is proposed. In this algorithm double-sided mesh is used for feature extraction, and the number of mesh is computed according to distance between and within classes, which proves the advantages of single-sided mesh compared with double-sided mesh. Then, training different Gaussian mixture model for different angle is to save the time used for entire image rotation process, simplifying the identification process. Improved Gaussian density judgment function is designed to further reduce time-consuming. The experimental results show that the algorithm can get 100% recognition rate while recognizing one banknote in 4 ms,guaranteeing accuracy and real-time performance.