A map-based normalized cross correlation algorithm using dynamic template for vision-guided telerobot

Vision-guided telerobot is often used to execute tasks, such as grasping and classification, in various environments, which contains some unfamiliar objects beyond its matching library. Hence, it is necessary to create new template dynamically for the unfamiliar objects. However, this procedure is inconvenient for the traditional template matching algorithm. In this article, a novel map–based normalized cross correlation algorithm is proposed. Map–based normalized cross correlation is summarized into two phases. In the learning phase, map–based normalized cross correlation creates new template and map by the superpixel-based GrabCut method dynamically, which is different from previous template matching algorithms. In the matching phase, a map-based similarity evaluation is designed to determine the position and rotation angle of object, where the map is used to eliminate the interference of background. Various experiments demonstrate that superpixel-based GrabCut method is more robust against noise than the traditional GrabCut algorithm and can separate the object from texture-rich background with less iteration times and time consumption. Additionally, map–based normalized cross correlation algorithm can locate objects in texture-rich images more accurately compared with polar transformation and image pyramids normalized cross correlation algorithm, especially for the matching of irregularly shaped object.