HSI空间和改进 C-means的彩色人民币号码分割方法

针对采集到的人民币号码图像都是彩色图像并携带有噪声这一现象，本文提出基于 HSI空间和改进的 C-means算法的人民币彩色号码图像分割方法。选用 HSI颜色空间作为彩色分割空间，在 HSI空间内，将 HSI的 3-D搜索问题转化为 3个 1-D的搜索问题，求取图像在 3个 1-D方向上的灰度直方图，该方法根据图像当前点 3×3邻域内每个像素灰度值与当前点灰度值差值的大小情况，确定聚类算法中当前点的灰度值 p(m)的值，采用 C-means聚类算法分别确定文字和非文字的聚类中心，利用欧式距离进行人民币号码前景和背景的聚类判断。该方法直接对彩色人民币号码图像进行分割，考虑了当前点与邻域像素点之间的相互关系，具有一定的自适应性。实验结果表明，提出的号码图像分割方法不受图像噪声和局部边缘变化的影响，且变换后数据量减少，易于计算，该方法对字母和数字的分割都有效，鲁棒性较强。

A Colorful RMB Number Image Segmentation Algorithm Based on the HSI Space and Improved C-means Cluster

Aiming at a phenomenon that the acquired RMB number is colorful and noised image, a method based on HIS space and improved clustering algorithm for RMB number color image segmentation is proposed. The HSI space is a colorful segmentation space, which is adopted. The 3-D searching problem is transformed into three 1-D searching problems in the HSI space. Three gray histograms on the 1-D direction is obtained. By the gray scale value of every pixel in current neighborhood 3×3 and the gray scale of the current pixel, the gray scale value p(m) of the current pixel ofcluster algorithm is determined, and improved C-means cluster method is used to distinguish the clustering center of character from non-character. The foreground and the background of RMB number image is clustering judged through using Euclidean distance. A colorful RMB number image is segmented and the segmentation method is adaptive. Experimental results show that the proposed segmentation method is not influenced by image noise and local edge change, and the amount of data is less than that of pre-transformation. This method is effective and robust for alphabet segmentation and number segmentation.