Abstract: To overcome the information loss in radical-projection-transform template matching algorithm, the proposed method introduces the clustering model parameters robust to linear illumination. The method eliminates the influence of illumination by linear contrast stretch, and takes the Gaussian mixture model clustering result as the template image feature. The matching errors are obtained by once iterative, and the iterative calculation can be implemented by using a simple look-up table. In searching stage, down sampling matching strategy is used to reduce computation cost and the average error of this stage is set to an adaptive threshold for precise matching. Then, the pixel-wise matching is operated in the neighborhood of the down sampling point whose error is less than the threshold, and finally the point of minimum error is set to be the target position. The test shows that the proposed method has better positioning accuracy and reliability than the normalized correlation based radical-projection-transform template matching algorithm.

Key words: template matching, Radical Projection Transform（RPT）, Gaussian mixture model, clustering feature, illumination robustness

摘要： 针对圆投影模板匹配方法特征提取过程中损失大量图像信息的缺点，提出了结合聚类模型参数的线性光照鲁棒圆投影模板匹配方法。所提方法采用线性对比度拉伸来消除光照影响，并将模板图像各圆环内像素点的高斯混合模型聚类参数作为模板特征。匹配时通过一次迭代计算即可得到匹配误差，且该匹配过程可通过查找表来提高匹配速度。在目标搜索时使用了降采样搜索方法，并将降采样搜索匹配后各位置的误差均值作为自适应阈值，对匹配误差小于该阈值的降采样点邻域进行逐点匹配，匹配误差最小的位置作为最终匹配结果。试验及分析说明所提方法的定位误差及可靠度与基于归一化相关及均值的圆投影匹配算法相比有较大提高。

关键词: 模板匹配, 圆投影变换, 高斯混合模型, 聚类特征, 光照鲁棒