Geodesic Distance-weighted Shape Vector Image Diffusion

Abstract--This paper presents a novel and efficient surface matching and visualization framework through the geodesic distanceweighted shape vector image diffusion. Based on conformal geometry, our approach can uniquely map a 3D surface to a canonical rectangular domain and encode the shape characteristics (e.g., mean curvatures and conformal factors) of the surface in the 2D domain to construct a geodesic distance-weighted shape vector image, where the distances between sampling pixels are not uniform but the actual geodesic distances on the manifold. Through the novel geodesic distance-weighted shape vector image diffusion presented in this paper, we can create a multiscale diffusion space, in which the cross-scale extrema can be detected as the robust geometric features for the matching and registration of surfaces. Therefore, statistical analysis and visualization of surface properties across subjects become readily available. The experiments on scanned surface models show that our...

• Type: Conference
• Year: 2008
• Where: VIS
• Authors: Jing Hua, Zhaoqiang Lai, Ming Dong, Xianfeng Gu, Hong Qin
• Keywords: Geodesic Distance-weighted Shape, IEEE Visualization, Shape Vector Image, Vector Image Diffusion, VIS 2008, Visualization