ALTOMS (Altomated Laser TrackingOptogenetic Manipulation System) is a real-time computer vision object recognitionlaser tracker. It uses a missile-like image recognitiontracking technology,uses several lasers to accurately hit the tracked object. The laser itself does not have the function of controlling nerves, but when we express light-sensitive proteins on specific nerve cells, we can stimulateinhibit nerves through lasers of specific wavelengths, which will affect the behavioral outcome.

"Coach Box" ideatechnology are derived from "CoachAI：金準羽球" project of the Ministry of ScienceTechnology "精準運科". The integrated technologies of "Coach Box" including computer vision, deep learning, machine learning, IoT, wearable applications, automation equipment,big data. The expected outcomes of "Coach Box" to show player performancegive expert advice through activities analysis of vision data. Simultaneously, it can be used as a medium for productive communication between coachesstudents.

The dialogue system is the main subsystem of the visually impaired navigation system, which provides destinations for the navigation system through multiple dialogues. We use the knowledge graph as the basis for reasoning. In terms of close-range navigation, deep learning technology is used to develop RGB camera detection depth algorithm, indoor semantic cutting algorithm, integrated detection depth estimationindoor semantic cutting in indoor obstacle avoidance, etc. The whole system uses the CellS software design framework to integrate distributed AIoT systems.

We invented a non-destructive terahertz (THz) deep-learning computed tomography system based on time-domain spectroscopy. In the method, THz time-domain signals are profiled. Multiple features are retrieved from those profiles by a trained modeltransformed to the spatial domain to reconstruct a cross-sectional tomographic image. We have also invented a 3D THz tomographic system based on multi-scale spatio-spectral feature fusion in a multi-scale manner. We believe our work will stimulate further applicable research of THz tomographic imaging with advanced computer vision techniques.

We propose a semantic-guided framework (FashionOn+) that generates image-based virtual try-on results with arbitrary poses. FashionOn+ contains three stages: (I) conducts the semantic segmentation to have the prior knowledge of body parts for rendering the corresponding texture in stage (II). (III) refines two salient regions, i.e., faceclothes, to generate high-quality results. With the novel architecture, we win first place in the Multi-pose Virtual Try-on Challenge in CVPR, 2020. Further, we tackle the low-resolution limitation (256x192)achieve high-resolution results (640x480).

Artificial intelligence 3D sensing image processing system based on array sensing Lidar aims to construct 3D image with high-quality immersion for AR/VR. The developed 3D scene recording system is based on the color camerahigh-accuracy chaotic LiDAR. The chaotic Lidar with APD arrayTOF sensors supports millimeter-accuracyinterference-avoiding capability within 100 meter in both indooroutdoor environments. High-performance embedded CNN processor supports high-throughput, high energy-efficient,low DRAM bandwidth computations for various image AI applications.