Traffic Sign Recognition Based on Two-dimensional Principal Component Analysis
基于二维主成分分析的交通标志牌识别

This paper proposed a feature extraction method for traffic sign recognition based on Two-Dimensional Principal Component Analysis (2DPCA). A series of experiments were performed on two traffic sign databases with the nearest neighbor classifier and Euler distance. One database is the image library in which images are obtained through a series of simulation transformation after image binarization, While another database is made up of images shot from real scenes through selecting many different location scenes. The method has a good effect on the recognition of the both image databases.