2020/08/24 by Youshan Zhang, Zhang, Youshan
Arts and Humanities · Computer Science · Engineering · Mathematics · #3D Shape Modeling and Analysis #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Forensic Anthropology and Bioarchaeology Studies #Morphological variations and asymmetry #cs.CV
paper · pdf · doi:10.48550/arxiv.2009.05108
BMVC 2020
openalex publication_date 2020/08/24 · arxiv created 2020/09/15 · arxiv updated 2020/09/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Geodesic regression has been proposed for fitting the geodesic curve. However, it cannot automatically choose the dimensionality of data. In this paper, we develop a Bayesian geodesic regression model on Riemannian manifolds (BGRM) model. To avoid the overfitting problem, we add a regularization term to control the effectiveness of the model. To automatically select the dimensionality, we develop a prior for the geodesic regression model, which can automatically select the number of relevant dimensions by driving unnecessary tangent vectors to zero. To show the validation of our model, we first apply it in the 3D synthetic sphere and 2D pentagon data. We then demonstrate the effectiveness of our model in reducing the dimensionality and analyzing shape variations of human corpus callosum and mandible data.