Detecting Markers in Blurred and Defocused Images

Planar markers enable an augmented reality (AR) system to estimate the pose of objects from images containing them. However, conventional markers are difficult to detect in blurred or defocused images. We propose a new marker and a new detection and identification method that is designed to work under such conditions. The problem of conventional markers is that their patterns consist of high-frequency components such as sharp edges which are attenuated in blurred or defocused images. Our marker consists of a single low-frequency component. We call it a mono-spectrum marker. The mono-spectrum marker can be detected in real time with a GPU. In experiments, we confirm that the mono-spectrum marker can be accurately detected in blurred and defocused images in real time. Using these markers can increase the performance and robustness of AR systems and other vision applications that require detection or tracking of defined markers.