An Improved Normalized Cross-correlation for Template Matching of Infrared Image
一种改进的红外图像归一化互相关匹配算法

A novel normalized cross-correlation paid (NCC) method for template matching of infrared image was proposed.Although the classical NCC method paid attention to global correlation for template matching,it ignored the correlation of rows and columns texture between template and image regions.Therefore,the classical NCC method might fail for template matching in complex scene.To improve its performance,the NCC computation was regarded as an optimization problem,which aimed to make algorithm most robust.And then the average in classical NCC formula was replaced by the optimization function of NCC reference value.So a novel NCC algorithm was brought forward,which was well suited for template matching of infrared image.The experimental results over real-world sequences show that the novel NCC algorithm is better than the classical algorithm in complex scene and is robust to the changes of background and target in template.