TSRNet: A Dual-Stream Network for Refining 3D Tooth Segmentation.

The field of 3D tooth segmentation has made considerable advances thanks to deep learning, but challenges remain with coarse segmentation boundaries and prediction errors. In this article, we introduce a novel learnable method to refine coarse results obtained from existing 3D tooth segmentation algorithms. The refinement framework features a dual-stream network called TSRNet (Tooth Segmentation Refinement Network) to rectify defective boundary and distance maps extracted from the coarse segmentation.

Real-Time 3D Facial Tracking via Cascaded Compositional Learning.

We propose to learn a cascade of globally-optimized modular boosted ferns (GoMBF) to solve multi-modal facial motion regression for real-time 3D facial tracking from a monocular RGB camera. GoMBF is a deep composition of multiple regression models with each is a boosted ferns initially trained to predict partial motion parameters of the same modality, and then concatenated together via a global optimization step to form a singular strong boosted ferns that can effectively handle the whole regression target. It can explicitly cope with the modality variety in output variables, while manifesting increased fitting power and a faster learning speed comparing against the conventional boosted ferns.

A Review on Automated Facial Nerve Function Assessment From Visual Face Capture.

Assessing facial nerve function from visible facial signs such as resting asymmetry and symmetry of voluntary movement is an important means in clinical practice. By using image processing, computer vision and machine learning techniques, replacing the clinician with a machine to do assessment from ubiquitous visual face capture is progressing more closely to reality. This approach can do assessment in a purely automated manner, hence opens a promising direction for future development in this field.