A novel pose invariant 3D dental biometrics framework is proposed for human identification by matching dental plasters in this paper. Using 3D overcomes a number of key problems that plague 2D methods. As best as we can tell, our study is the first attempt at 3D dental biometrics. It includes a multi-scale feature extraction algorithm for extracting pose invariant feature points and a triplet-correspondence algorithm for pose estimation. Preliminary experimental result achieves 100% rank-1 accuracy by matching 7 postmortem (PM) samples against 100 ante-mortem (AM) samples. In addition, towards a fully automated 3D dental identification testing, the accuracy achieves 71.4% at rank-1 accuracy and 100% at rank-4 accuracy. Comparing with the existing algorithms, the feature point extraction algorithm and the triplet-correspondence algorithm are faster and more robust for pose estimation. In addition, the retrieval time for a single subject has been significantly reduced. Furthermore, we discover that the investigated dental features are discriminative and useful for identification. The high accuracy, fast retrieval speed and the facilitated identification process suggest that the developed 3D framework is more suitable for practical use in dental biometrics applications in the future. Finally, the limitations and future research directions are discussed.

Free-space dilation is an effective approach for narrow passage sampling, a well-recognized difficulty in probabilistic roadmap (PRM) planning. Key to this approach are methods for dilating the free space and for determining the amount of dilation needed. This paper presents a new method of dilation by shrinking the geometric models of robots and obstacles. Compared with existing work, the new method is more efficient in both running time and memory usage. It is also integrated with collision checking, a key operation in PRM planning. The efficiency of the dilation method enables a new PRM planner which quickly constructs a series of dilated free spaces and automatically determine the amount of dilation needed. Experiments show that both the dilation method and the planner work well in complex geometric environments. In particular, the planner reliably solved the most difficult version of the alpha puzzle, a benchmark test for PRM planners

Quality surface meshes for molecular models are desirable in the studies of protein shapes and functionalities. However, there is still no robust software that is capable to generate such meshes with good quality. In this paper, we present a Delaunay-based surface triangulation algorithm generating quality surface meshes for the molecular skin model. We expand the restricted union of balls along the surface and generate an /spl epsiv/-sampling of the skin surface incrementally. At the same time, a quality surface mesh is extracted from the Delaunay triangulation of the sample points. The algorithm supports robust and efficient implementation and guarantees the mesh quality and topology as well. Our results facilitate molecular visualization and have made a contribution towards generating quality volumetric tetrahedral meshes for the macromolecules.

We present an efficient algorithm to mesh the macromolecules surface model represented by the skin surface defined by Edelsbrunner. Our algorithm overcomes several challenges residing in current surface meshing methods. First, we guarantee the mesh quality with a provable lower bound of 21/spl deg/ on its minimum angle. Second, we ensure the triangulation is homeomorphic to the original surface. Third, we improve the efficiency of constructing the restricted Delaunay triangulation (RDT) of smooth surfaces. We achieve this by constructing the RDT using the advancing front method without computing the Delaunay tetrahedrization of the sample points on the surfaces. The difficulty of handling the front collision problem is tackled by employing the Morse theory. In particular, we construct the Morse-Smale complex to simplify the topological changes of the front. Our implementation results suggest that the algorithm decrease the time of generating high quality homeomorphic skin mesh from hours to a few minutes.

The construction of shape spaces is studied from a mathematical and a computational viewpoint. A program is outlined, reducing the problem to four tasks: the representation of geometry, the canonical deformation of geometry, the measuring of distance in shape space, and the selection of base shapes. The technical part of the paper focuses on the second task: the specification of a deformation mixing two or more shapes in continuously changing proportions.