Xiao, Pengdong; Barnes, Nick; Lieby, Paulette; Caetano, Tiberio
A dense point-based registration is an ideal starting point for detailed comparison between two neuroanatomical objects. This paper presents a new algorithm for global dense point-based registration between anatomical objects without assumptions about their shape. We represent mesh models of the surfaces of two similar 3D anatomical objects using a Markov Random Field and seek correspondence pairs between points in each shape. However, for densely sampled objects the set of possible point by...[Show more]
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