Recently, research fields of augmented reality and robot navigation are actively investigated. Estimating a relative posture between an object and a camera is an important task in these fields. In this paper, we propose a novel method for posture estimation by using high frequency markers and kernel regressions. The markers are embedded in an object's texture in the high frequency domain. We observe the change of spatial frequency of object's texture to estimate a current posture of the object. We conduct experiments to show the effectiveness of our method.
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