基于极坐标系下二维直方图的图像分割算法
Image Segmentation Algorithm Based on Two-Dimensional Histogram in Polar Coordinate System

基于二维直方图的图像分割算法存在明显误分, 且利用二维Renyi熵求解最佳阈值计算量过大．为解决这些问题, 提出基于极坐标系下二维直方图的图像分割算法．首先, 将图像各像素点表示在极坐标系中, 根据各点的极角区分噪声点和非噪声点; 然后对噪声点进行平滑处理, 处理之后图像各像素点都集中在极坐标系中极角为45°的极径附近．由于滤噪后各像素点的极角差别很小, 所以仅利用各点的极径信息即可进行分割阈值的选取, 由此将二维问题转化为一维问题, 以减少计算量．实验结果表明, 该算法分割效果良好, 尤其适用于受噪声污染较严重的图片, 而且与传统二维算法及其改进算法相比, 运行速度有很大提高．
There is obvious wrong segmentation in the image segmentation algorithm which is based on the two-dimensional histogram，and the computational load of solving the optimal threshold by using two-dimensional Renyi entropy is too large. To solve these problems，an image segmentation algorithm based on two-dimensional histogram in polar coordinate system was proposed. Firstly，the pixels of the image were represented in polar coordinate system，and the noise points and non-noise points were distinguished according to their polar angles; then the noise points were smoothed. After this procedure，all the pixels of the image were concentrated around the polar axis with a polar angle of 45 degrees. Since the differences among the polar angles of these pixels are very small，the segmentation threshold can be selected by using the polar radius information of each point. Thus，the two-dimensional problem is converted into a one-dimensional problem to reduce the computational load. The experimental results show that the algorithm is effective in image segmentation，especially for images with serious noise pollution. Moreover，compared with the traditional two-dimensional algorithm and its improved algorithm，the running speed of this algorithm has been greatly improved