Fig. 1.

Schematic explanation for correspondence detection, probability estimation, and neighborhood matching in our registration algorithm. A template point x is tentatively warped to a location h(x) in the subject. In the subject space, a neighboring point v of h(x) is considered as a candidate correspondence for the template point x, with the probability p_x,z calculated from the similarity of attribute vectors on points x and v, as well as the similarity of attribute vectors in the respective neighborhoods (e.g., a dotted grey circle n₂ in the template and one deformed dotted grey circle around point v in the subject). The deformed dotted circle around point v is shifted by v−h(x) from the one around point h(x), which is a warped version of an original circle in the template. In the searching neighborhood n₁ (red circle), only several points (green cross points) are detected as candidate correspondences for template point x.