We propose two types of novel morphological metrics for quantifying the geometry of tubular structures on computed tomography (CT) images. We apply our metrics to identify irregularities in the airway of patients with chronic obstructive pulmonary disease (COPD) and demonstrate that they provide complementary information to the conventional metrics used to assess COPD, such as the tissue density distribution in lung parenchyma and the wall area ratio of the segmented airway. The three-dimensional shape of the airway and its abstraction as a rooted tree with the root at the trachea carina are automatically extracted from a lung CT volume, and the two metrics are computed based on a mathematical tool called persistent homology; treeH0 quantifies the distribution of branch lengths to assess the complexity of the tree-like structure and radialH0 quantifies the irregularities in the luminal radius along the airway. We show our metrics are associated with clinical outcomes.

As one of the facial expression recognition techniques for Head-Mounted Display (HMD) users, embedded photo-reflective sensors have been used. In this paper, we investigate how gaze and face directions affect facial expression recognition using the embedded photo-reflective sensors. First, we collected a dataset of five facial expressions (Neutral, Happy, Angry, Sad, Surprised) while looking in diverse directions by moving 1) the eyes and 2) the head. Using the dataset, we analyzed the effect of gaze and face directions by constructing facial expression classifiers in five ways and evaluating the classification accuracy of each classifier. The results revealed that the single classifier that learned the data for all gaze points achieved the highest classification performance. Then, we investigated which facial part was affected by the gaze and face direction. The results showed that the gaze directions affected the upper facial parts, while the face directions affected the lower facial parts. In addition, by removing the bias of facial expression reproducibility, we investigated the pure effect of gaze and face directions in three conditions. The results showed that, in terms of gaze direction, building classifiers for each direction significantly improved the classification accuracy. However, in terms of face directions, there were slight differences between the classifier conditions. Our experimental results implied that multiple classifiers corresponding to multiple gaze and face directions improved facial expression recognition accuracy, but collecting the data of the vertical movement of gaze and face is a practical solution to improving facial expression recognition accuracy.

This paper presents a thin digital full-face mask display that can reflect an entire facial expression of a user onto an avatar to support augmented face-to-face communication in real environments. Although camera-based facial expression recognition technology has enabled people to augment their faces with avatars, application was limited to face-to-face communication in virtual environments. To enable digital facial augmentation with an avatar in a real space, we propose a digital face mask display system that integrates a lightweight flexible display with a thin facial expression recognition system. The thin wearable facial expression recognition system was implemented with photo reflective sensor arrays which can measure facial expressions at 40 feature points distributed across an entire face. We investigated a ten-class facial expression identification model based on an SVM training algorithm. The trained model achieved an average accuracy of 79% when identifying the facial expressions of multiple users. User experiments indicated that the proposed thin digital full-face mask display allows the wearer to control the facial expression of the avatar with a fast response rate and create a positive sense of self-agency and self-ownership toward the augmented avatar face.

We propose a facial expression mapping technology between virtual avatars and Head-Mounted Display (HMD) users. HMD allow people to enjoy an immersive Virtual Reality (VR) experience. A virtual avatar can be a representative of the user in the virtual environment. However, the synchronization of the the virtual avatar's expressions with those of the HMD user is limited. The major problem of wearing an HMD is that a large portion of the user's face is occluded, making facial recognition difficult in an HMD-based virtual environment. To overcome this problem, we propose a facial expression mapping technology using retro-reflective photoelectric sensors. The sensors attached inside the HMD measures the distance between the sensors and the user's face. The distance values of five basic facial expressions (Neutral, Happy, Angry, Surprised, and Sad) are used for training the neural network to estimate the facial expression of a user. We achieved an overall accuracy of 88% in recognizing the facial expressions. Our system can also reproduce facial expression change in real-time through an existing avatar using regression. Consequently, our system enables estimation and reconstruction of facial expressions that correspond to the user's emotional changes.

This paper proposes a remote welding robot manipulation system by using multi-view images. After an operator specifies two-dimensional path on images, the system transforms it into three-dimensional path and displays the movement of the robot by overlaying graphics with images. The accuracy of our system is sufficient to weld objects when combining with a sensor in the robot. The system allows the non-expert operator to weld objects remotely and intuitively, without the need to create a 3D model of a processed object beforehand.