A model based approach for pose estimation and rotation invariant object matching

Pose estimation has been considered to be an important component in many pattern recognition and computer vision systems. In this paper, we introduce a pose estimation method based on implicit polynomials. We also introduce a set of rotation invariant measures for object matching. We test both methods under colored noise, missing points, and affine transformation. Extensive testings indicate that our methods work fairly well under various disturbances. We also compare our methods with existing ones in the literature.