Research On Comparison Of3D Craniofacial Shape Similarities

Computer aided craniofacial reconstruction, an emerging technology based on modern anatomy and traditional technologies, is used to reshape the face of a living person or to restore the face on the skull of a dead. This technology has wide development prospects in many fields such as archaeology, anthropology, iatrology and public safety system. So far some progresses have been made in the study of construction, but construction effects evaluation is still a challenging and difficult scheme. A vital part of evaluation is comparison of craniofacial shape similarities, which can set the stage for the qualitative and quantitative analysis for construction effects and can further be used in cranioface screening and retrieval. This thesis is concerned with comparison of3D craniofacial shape similarities. According to the characteristics of3D craniofaces, this thesis investigates on a series of methods to extract the shape features of3D craniofaces and then to compare them. The main work of this thesis is summarized as follows:1. A combined partial symmetry and moment balance method for pose estimation is presented. First CPCA coordinate planes of a3D model are computed to establish the model's symmetry planes and its initial pose. Then a new measure, which is called partial symmetry length ratio (PSLR), is introduced to judge whether the model is partial symmetry or not. If the model is partial symmetry, the pose with maximal PSLR is the estimated pose; otherwise moment balance is used to estimate the model's final pose.2. A method of pose estimation for3D craniofaces is introduced based on the above method. When estimating poses of3D craniofaces, their faces need to be aligned at first. The median plane is selected to solve this problem. For a cranioface the contour of its median plane is computed. Corresponding to a fixed angular sampling interval the contour is resampled to get a set of points, which can represent the shape of the median plane. The point set is aligned to another well-aligned set to get the final pose of the cranioface. The method was used to estimate the poses of all craniofaces in our database.3. A method of comparison of3D craniofacial shape similarities based on principal warps is proposed. In our approach principal warps are extended to analyze shape deformation between the homologous landmarks of3D models. And comparison of two craniofaces is regarded as the deformation of the referenced one into the other. The more similar they are, the smaller deformation is. First the homologous landmarks are selected from the two craniofaces. Then thin-plate spline is used to establish a map between them and to compute a bending transformation matrix. The matrix can be represented by new bases, which are the principal warps of the referenced cranioface. And on that basis, the similarity formula is defined to compare craniofacial shape similarities.4. A method of comparison of craniofacial similarities based on map projection is developed. Mercator projection, which is commonly used for navigation, is introduced to map3D cranioface on a flat surface. Then geodetic height is represented the curvature of the surface. Thus a developed curving surface is got and is divided into small cells. An improved cone-curvature estimation method is developed to compute the curvature of the vertices in the cells. Four measures, which is height mean, height variance, cone-curvature mean and cone-curvature variance, are used to define the distance between each cell, and further to define the distance between two curving surfaces. Craniofacial similarities are compared by the distance.5. A shape descriptor, self-difference shape descriptor (SDSD), is proposed to describe the shape of3D model. A set of parallel planes with equal interval are selected to cut a model. Each plane intersects with the model and the set of the intersection points is called a slice. All slices will represent the shape of the model. The difference of two neighboring slices will reflect local shape variation of the model. Therefore SDSD is defined as a vector, whose component consists of the difference. SDSD can represent shape variation trend of the model. Supposing different models have different shape variation trends, this descriptor can be used to represent3D models. Lastly a computing method is given to compare similarities of craniofaces.6. A method of radius relative angle-context distribution (RRACD) is introduced to solve the problems in traditional RACD methods, such as unstable results and long-time computation. The normal of the symmetry plane of a cranioface is regarded as the principal axis instead of the first PCA axis. Then a group of concentric spherical shells are constructed to partition the vertices of the cranioface into different shell sections. For each section the relative angle-context distribution is defined by relative angles of the vertices inside it. Thus RRACD is the distributions of all sections. Finally chi-square distance is used to measure craniofacial distance and to compare craniofaces.