Robust template matching with large angle localization

Template matching is an important solution for the object detection in instance-level. Many kinds of matching methods have been utilized to find the template position in the target image. But most of them ignore the matching angle, which causes the result can't satisfy the detection accuracy when the object has a big in-plane rotation. In this paper, we propose a robust method for template matching with large angle localization. The basic idea is to iteratively search the corresponding patch-features and updating the template location with rotation transformation. The method firstly employs the intensity centroid to rectify local patches as rotation-invariant features. And then, Best-Buddies Pairs (BBPs) are extracted to find corresponding features between template and target images. To further enhance the robustness against outliers, a robust objective function is presented to register features based on the Maximum Correntropy Criterion and optimized for the transformation with translation and rotation parameters. Experimental results demonstrate the effectiveness and robustness of the proposed method.