Content area
Full text
Introduction
Telerobot is a special type of robot, which is widely applied in space stations, undersea detections, telemedicine facilities, and other remote control devices.[1],[2] To improve the efficiency of telerobot and release the workload of operator, some smart systems have been designed and applied to tasks including force feedback teleoperation,[3]-[5] to autonomous obstacle avoidance, and so on.[6],[7]
Vision-based positioning, as an important machine vision technology, is increasingly applied in telerobot systems.[8]-[10] It makes telerobot achieve accurate grasping and efficient classification automatically. Pixel-based template matching is one of the most popular methods to determine the target position and rotation angle.[11]-[13]
In the past decades, various pixel-based template matching algorithms have been investigated. These algorithms work as follows: given a template image [Formula Omitted: See PDF] and a target image [Formula Omitted: See PDF], find the best match for [Formula Omitted: See PDF] in the image [Formula Omitted: See PDF] that has the minimum distortion or the maximum correlation.[14] Global search (GS), as a fundamental technique, is time consuming.[15] There are also other measures of score function, such as the sum of squared differences (SSD),[16] the sum of absolute differences (SAD),[17] sequential similarity detection algorithm (SSDA),[18] partial distortion elimination (PDE),[19] and sorting-based partial distortion elimination (SB-PDE).[20] They use the absolute or Euclidean distance as the similarity measure, which means poor matching accuracy and robustness against noise. Fortunately, the normalized cross correlation (NCC) algorithm,[21] compensating for both additive and multiplicative variations under uniform illumination changes,[22] was proposed. To improve the computation efficiency, pyramid methods were proposed, which have been widely used in pixel-based template matching.[23],[24]
In addition, if the object in target image is rotated with respect to the template, the polar transformation algorithm will be used to reduce the computing load.[25]-[27] When a polar transformation is performed, a circular template is needed. However, there is an issue when the circular template is selected for the elongated or irregularly shaped object such as bolts, rivets, chips. If the incircle region is selected, then the template will not contain all of pixels belonging to the object. And if the excircle region is selected, some pixels out of the object will be contained....