A contour-based separation of vertically attached traffic signs

This paper presents a contour-based approach to separate vertically attached traffic signs. The algorithm is based on using binary images which are generated by any color segmentation algorithm to represent objects which could be candidate traffic signs. Since all traffic signs are similar about their vertical axis, an improved cross-correlation algorithm is invoked to determine this similarity and filters traffic sign candidates. Shape decomposition is used to smooth the contour of the candidate object iteratively in order to reduce white noise. Flipping point detection algorithm which locates black noise along the smoothed contour and the curve prediction algorithm are invoked to determine the final cut points. A separation accuracy of 94% is achieved by the algorithm. In this experiment more that 70000 images of different traffic sign combinations are invoked to achieve this result. The algorithm is tested on one-sign images, two-sign images, and three-sign images which are combined together for the purpose of testing this algorithm.