Object recognition by alignment using invariant projections of planar surfaces

This paper presents an efficient and robust alignment algorithm for recognizing 3D objects. We first show that for features from planar surfaces which undergo linear transformations in space, there exists a class of transformations that yield projections invariant to the surface motions, up to rotations in the image field. To use this property in recognition, we propose a new alignment approach based on centroid alignment of corresponding feature groups built on these invariant projections of planar surfaces. This method uses only a single pair of 2D model and data pictures for recognizing a 3D object. Some results on natural pictures are given.