AFFINE MATCHING OF PLANAR SETS

To recognize an object in an image, we must determine the best-fit tra nsformation which maps an object model into the image data. In this pa per, we propose a new alignment approach to recovering those parameter s, based on centroid alignment of corresponding feature groups built i n the model and data. To derive such groups of features, we exploit a clustering technique that minimizes intraclass scatter in coordinates that have been normalized up to rotations using invariant properties o f planar patches. The present method uses only a single pair of 2D mod el and data pictures even though the object is 3D. Experimental result s both through computer simulations and tests on natural pictures show that the proposed method can tolerate considerable perturbations of f eatures including even partial occlusions of the surface. (C) 1998 Aca demic Press.