Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Spatial transformation and registration of brain images using elastically deformable models
Computer Vision and Image Understanding
Fully Automatic Registration of 3D Point Clouds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Anatomically-Aware, Automatic, and Fast Registration of 3D Ear Impression Models
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Automatic detection of anatomical features on 3D ear impressions for canonical representation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
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We propose a method for the deformable registration of organic surfaces. Meaningful correspondences between a source surface and a target surface are established by means of a rich surface descriptor that incorporates three categories of features: (1) local and regional geometry; (2) surface anatomy; and (3) global shape information. First, surface intrinsic, geodesic distance integrals, are exploited to constrain the global geodesic layout. Consequently, the resulting transformation ensures topological consistency. Local geometric features are then introduced to enforce local conformity of various regions. To this end, the extrema of appropriate curvatures -- the extrema of mean curvature, minima of Gauss and minimum principal curvature, and the maxima of maximum principal curvature -- are considered. Regional features are introduced through curvature integrals over various scales. On top of this, explicit anatomical priors are included, thereby resulting in anatomically more consistent registration. The source surface is deformed to the target by minimizing the energy of matching the source features to the target features under a Gaussian propagation model. We validate the proposed method with application to the outer ear surfaces.