Learning A Shared Transform Model for Skull to Digital Face Image Matching

BFSM：3D双向面部-颅骨可变形模型

BFSM: 3D Bidirectional Face-Skull Morphable Model

针对配对面颅数据稀缺与畸形样本不足，本文构建200余例多模态数据集，提出密集射线配准与BFSM双向可变形模型，实现面颅互推、一对多重建及临床应用。

On Matching Skulls to Digital Face Images: A Preliminary Approach

Forensic application of automatically matching skull with face images is an important research area linking biometrics with practical applications in forensics. It is an opportunity for biometrics and face recognition researchers to help the law enforcement and forensic experts in giving an identity to unidentified human skulls. It is an extremely challenging problem which is further exacerbated due to lack of any publicly available database related to this problem. This is the first research in this direction with a two-fold contribution: (i) introducing the first of its kind skull-face image…

Twin-S：颅底手术的数字孪生系统

Twin-S: A Digital Twin for Skull-base Surgery

Twin-S是用于颅底手术的数字孪生框架，结合高精度光学跟踪与实时仿真，以帧率更新钻削过程，平均误差1.39毫米，并通过混合现实增强态势感知。

IDraw：基于数字绘画图像的艺术家身份验证

IDraw: Artist Verification from Digital Drawing Images

该研究针对数字绘画作者身份验证的挑战，提出IDraw框架，构建首个多模态数据集，可从完整图像推断绘画行为、抑制内容干扰，在9种主干网络下将验证误差最多降低40%。

Transfer Learning from an Artificial Radiograph-landmark Dataset for Registration of the Anatomic Skull Model to Dual Fluoroscopic X-ray Images

Registration of 3D anatomic structures to their 2D dual fluoroscopic X-ray images is a widely used motion tracking technique. However, deep learning implementation is often impeded by a paucity of medical images and ground truths. In this study, we proposed a transfer learning strategy for 3D-to-2D registration using deep neural networks trained from an artificial dataset. Digitally reconstructed radiographs (DRRs) and radiographic skull landmarks were automatically created from craniocervical CT data of a female subject. They were used to train a residual network (ResNet) for landmark detecti…

Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model

As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model. S2FL is capable of decomposing mult…