The system, “Occluded Resistant Face DetectionRecognition System”, contains different scale detectors for calculating face locations. Additive angular margin loss is added into the training phase for achieving high efficiencyaccuracy. The system can achieves 80 accuracy under the 50 face occluded.

This study built an automatic insect pest image identification system based on tiny Yolov3 deep learning model. By optimizing the tiny Yolov3 detection model, images of insect pests on scanned sticky paper can be automatically identified. The system achieves a testing accuracy of 0.93, 0.90 for whitefliesthrips respectively.

Integrating deep learning, 3D information analysis, hyperspectral analysis, computer vision analysiscombined the multi-source images to develop fruit quality monitoring techniques, including: planting area monitoring, position monitoring, yield monitoring, harvesting time prediction, fruit maturity measurement, fruit quality testing, to achieve the goal of enhancing industrial value.

"1. Use PE Cluster to make the calculation reconfigurable,can be modularized into 36X PEs, supporting up to 2160PEs 2. It can be configured as an independent IP with high computing throughputlow power consumption. The computing performance can reach 864 GOPS@200MHz when 2160PEs are employed."

Our technique is a liver tumor detection method using machine learning which is the most popular AI technique in these years. the high-magnification image provides more details, but the view is smaller in contrast, a low-magnification image has the larger view but less details. Thus, our method combines the two magnificationsyields more accurate tumor detection results.

Deep-Learning-Based Pupil Center DetectionTracking Technology for Visible-Light Wearable Gaze Tracking Devices

The computer-aided nodule detection system in CT image consists of the search sliding window, YOLOv2 architecture, 3-D CapsNet, skip connection,post-processing. First, the CT image is divided into numerous VOIs by sliding window. Second, a 3-D CapsNet based on YOLOv2 architectureskip connection is applied to the VOIs for classifying VOIs as nodulenot. Finally, the non-maximum suppression algorithm is performed to decide the final detection result.

“MeDA OXR: Real-time Hazard Recognition System,” which can automatically screeninterpret the medical images in real-time, is designed for emergency rooms to assist physicians in diagnosis, reducing medical risks,improving overall efficiency. Combining portable X-RayAI algorithms, the system performs real-timeaccurate preliminary screeningdiagnoses of diseases, such as pneumothorax, pneumonia,tuberculosis. It can also locate the nasogastric tube, endotracheal tube,central venous catheter, while misplacement of that is sent to the physicians when detected.

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.

PANCREASaver contains a “PC automatic segmentation model” (image segmentation)a “PC analysis AI model” (image classification) that can read the DICOM format of postcontrast CT images directly for the automatic analysis process. After conducting prep-processing with image processing algorithms, C2FNAS is employed to illustrate the tumor position prior to the diagnosis conducted by CNN. The results can be provided to the physician for diagnostic reference so as to reduce early omissionsincrease the detection rate of pancreatic cancer.

High-accuracy 3D sensingAI image processing system for constructing high-quality immersion 3D image for AR/VR. Chaotic Lidar with APD arrayTOF sensors supports millimeter-accuracyinterference-avoiding capability. High-performance CNN processor supports high-performancelow DRAM bandwidth computations for various image AI applications.