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With the outbreak of COVID-19, epidemic prevention has become a way to prevent the spread of epidemics. Many public places, such as hospitals, schools, and office places, require disinfection and temperature measurement. To implement epidemic prevention systems and reduce the risk of infection, it is a recent trend to measure body temperature through non-contact sensing systems with thermal imaging cameras. Compared to fingerprints and irises, face recognition is accurate and does not require close contact, which significantly reduces the risk of infection. However, masks block most facial features, resulting in the low accuracy of face recognition systems. This work combines masked face recognition with a thermal imaging camera for use as an automated attendance system. It can record body temperature and recognize the person at the same time. Through the designed UI system, we can search the attendance information of each person. We not only provide the design method based on convolutional neural networks (CNNs), but also provide the complete embedded system as a real demonstration and achieve a 94.1% accuracy rate of masked face recognition in the real world. With the face recognition system combined with a thermal imaging camera, the purpose of screening body temperature when checking in at work can be achieved. [ABSTRACT FROM AUTHOR]

Given the huge demand for wire in today's society, the quality of the wire is especially required. To control the quality of the produced wire, the industry has a great desire for automated optical inspection technology. This technology is a high-speed and highly accurate optical image inspection system that uses mechanical sensing equipment to replace the human eye as the inspection method and simulates manual operation by means of a robotic arm. In this paper, a high-performance algorithm for the automated optical inspection of wire color sequence is proposed. This paper focuses on the design of a high-speed wire color sequence detection that can automatically adapt to different kinds of wires and recognition situations, such as a single wire with only one color, and one or two wires covered with aluminum foil. To be further able to successfully inspect even if the wire is short in the screen and the two wires are close to each other, we calculate the horizontal gradient of the wires by edge detection and morphological calculation and identify the types and color sequences of the wires in the screen by a series of discriminative mechanisms. Experimental results show that this method can achieve good accuracy while maintaining a good computation speed. [ABSTRACT FROM AUTHOR]

With advancements in technology, more and more research is being focused on enhancing daily life quality and convenience. Along with the increase in the development of gesture control systems, many controllers, such as the keyboard, mouse, and other devices, have been replaced with remote control products, which are gradually becoming more intuitive for users. However, vision-based hand gesture recognition systems still have many problems to overcome. Most hand detection methods adopt a skin filter or motion filter for pre-processing. However, in a noisy environment, it is not easy to correctly extract interesting objects. In this paper, a VLSI design with dual-cameras has been proposed to construct a depth map with a stereo matching algorithm and recognize hand gestures. The proposed system adopts an adaptive depth filter to separate interesting foreground objects from the background. We also propose dynamic gesture recognition using depth and coordinate information. The system can perform static and dynamic gesture recognition. The ASIC design is implemented in TSMC 90 nm with about 47.3 K gate counts, and 27.8 mW of power consumption. The average accuracy of each gesture recognition is 83.98%. [ABSTRACT FROM AUTHOR]

A new method for the detection of important scenes in baseball videos via a time-lag-aware multimodal variational autoencoder (Tl-MVAE) is presented in this paper. Tl-MVAE estimates latent features calculated from tweet, video, and audio features extracted from tweets and videos. Then, important scenes are detected by estimating the probability of the scene being important from estimated latent features. It should be noted that there exist time-lags between tweets posted by users and videos. To consider the time-lags between tweet features and other features calculated from corresponding multiple previous events, the feature transformation based on feature correlation considering such time-lags is newly introduced to the encoder in MVAE in the proposed method. This is the biggest contribution of the Tl-MVAE. Experimental results obtained from actual baseball videos and their corresponding tweets show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]