Efficient computation of scale-space features for deformable shape correspondences
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Physical Scale Keypoints: Matching and Registration for Combined Intensity/Range Images
International Journal of Computer Vision
On the Geometry of Multivariate Generalized Gaussian Models
Journal of Mathematical Imaging and Vision
Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds
International Journal of Computer Vision
Ricci flow-based spherical parameterization and surface registration
Computer Vision and Image Understanding
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This paper presents a novel and efficient surface matching and visualization framework through the geodesic distance-weighted 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 method is very robust for feature extraction and surface matching even under noise and resolution change. We have also applied the framework on the real 3D human neocortical surfaces, and demonstrated the excellent performance of our approach in statistical analysis and integrated visualization of the multimodality volumetric data over the shape vector image.