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Computer-aided classification serves as the basis of virtual cultural relic management and display. The majority of the existing cultural relic classification methods require labelling of the samples of the dataset; however, in practical applications, there is often a lack of category labels of samples or an uneven distribution of samples of different categories. To solve this problem, we propose a 3D cultural relic classification method based on a low dimensional descriptor and unsupervised learning. First, the scale-invariant heat kernel signature (Si-HKS) was computed. The heat kernel signature denotes the heat flow of any two vertices across a 3D shape and the heat diffusion propagation is governed by the heat equation. Secondly, the Bag-of-Words (BoW) mechanism was utilized to transform the Si-HKS descriptor into a low-dimensional feature tensor, named a SiHKS-BoW descriptor that is related to entropy. Finally, we applied an unsupervised learning algorithm, called MKDSIF-FCM, to conduct the classification task. A dataset consisting of 3D models from 41 Tang tri-color Hu terracotta Eures was utilized to validate the effectiveness of the proposed method. A series of experiments demonstrated that the SiHKS-BoW descriptor along with the MKDSIF-FCM algorithm showed the best classification accuracy, up to 99.41%, which is a solution for an actual case with the absence of category labels and an uneven distribution of different categories of data. The present work promotes the application of virtual reality in digital projects and enriches the content of digital archaeology.

The emergence of the three-dimensional (3D) scanner has greatly benefited archeology, which can now store cultural heritage artifacts in computers and present them on the Internet. As many Terracotta Warriors have been predominantly found in fragments, the pre-processing of these fragments is very important. The raw point cloud of the fragments has lots of redundant points; it requires an excessively large storage space and much time for post-processing. Thus, an effective method for point cloud simplification is proposed for 3D Terracotta Warrior fragments. First, an algorithm for extracting feature points is proposed that is based on local structure. By constructing a k-dimension tree to establish the k-nearest neighborhood of the point cloud, and comparing the feature discriminant parameter and characteristic threshold, the feature points, as well as the non-feature points, are separated. Second, a deep neural network is constructed to simplify the non-feature points. Finally, the feature points and the simplified non-feature points are merged to form the complete simplified point cloud. Experiments with the public point cloud data and the real-world Terracotta Warrior fragments data are designed and conducted. Excellent simplification results were obtained, indicating that the geometric feature can be preserved very well.

Cone-beam X-ray luminescence computed tomography (CB-XLCT) emerged as a novel hybrid technique for early detection of small tumors in vivo . However, severe ill-posedness is still a challenge for CB-XLCT imaging. In this study, an adaptive shrinking reconstruction framework without a prior information is proposed for CB-XLCT. In reconstruction processing, the mesh nodes are automatically selected with higher probability to contribute to the distribution of target for imaging. Specially, an adaptive shrinking function is designed to automatically control the permissible source region at a multi-scale rate. Both 3D digital mouse and in vivo experiments were carried out to test the performance of our method. The results indicate that the proposed framework can dramatically improve the imaging quality of CB-XLCT., Competing Interests: The authors declare no conflicts of interest., (© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L 1 minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of L 1 and L 2 norms. To solve the nonconvex L 1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L 1 minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for L 1-2 minimization outperforms the representative algorithms for L 1 , L 2 , L 1/2 , and L 0 when the system matrix is highly coherent., Competing Interests: The authors declare that there is no conflict of interests regarding the publication of this paper.