Skull sex estimation is one of the hot research topics in forensic anthropology, and has important research value in the fields of criminal investigation, archeology, anthropology, and so on. Sex estimation of skull is crucial in forensic investigations, whether in legal situations that involve living people or to identify mortal remains. The aim of this study is to establish a skull‐based sex estimation model in Chinese population, providing a scientific reference for the practical application of forensic medicine and anthropology. We take the superior orbital margin and frontal bone of the skull as the research object and proposed a technology of objective sex estimation of the skull using wavelet transform and Fourier transform. Firstly, the supraorbital margin and frontal bone were quantified by wavelet transform and Fourier transform, and then the extracted features were classified by SVM, and the model was tested. The experimental results show that the accuracy rate of male and female sex discrimination is 90.9% and 94.4%, respectively, which is higher than that of morphological and measurement methods. Compared with the traditional methods, the method has more theoretical basis and objectivity, and the correct rate is higher.

The image classification task of porcelain fragments is of great significance for the digital preservation of cultural heritage. However, common issues are encountered in the image processing of porcelain fragments, including the low computation speed, decreased accuracy due to the uneven distribution of sample categories, and model instability. This study proposes a novel Capsule Network model, referred to as LBCapsNet, which is suitable for the extraction of features from images of porcelain artifacts fragments. A bottleneck-like channel transformation module denoted by ChannelTrans, which resides between the convolutional layer and the PrimaryCaps layer, was first designed. This module is used to reduce the computational complexity and enhance the processing speed when dealing with intricate porcelain images. The MF-R loss function was then proposed by incorporating focal loss into the original loss function. This allows to address the issue of imbalanced distribution of ceramic shard samples and reduce the classification errors, which leads to faster convergence with smoother trend. Finally, an adaptive dynamic routing mechanism is designed with a dynamic learning rate to enhance the overall stability of the classification process. The experimental results obtained on public datasets, such as MNIST, Fashion- MNIST, CIFAR10, FMD and DTD as well as porcelain fragments dataset, demonstrate that LBCapsNet achieves high classification accuracy with faster and more stable computation compared with existing methods. Furthermore, the ability of LBCapsNet to process special textures can provide technical support for the digital preservation and restoration of cultural heritage.

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.