The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Overactivation of microglial cells may cause severe brain tissue damage in various neurodegenerative diseases. Therefore, the overactivation of microglia should be repressed by any means. The present study investigated the potential mechanism and signaling pathway for the repressive effect of TGF-β1, a major anti-inflammatory cytokine, on overactivation and resultant death of microglial cells. A bacterial endotoxin LPS stimulated expression of inducible NO synthase (iNOS) and caused death in cultured microglial cells. TGF-β1 markedly blocked these LPS effects. However, the LPS-evoked death of microglial cells was not solely attributed to excess production of NO. Because phosphatidylinositol 3-kinase (PI3K) was previously shown to play a crucial role in iNOS expression and cell survival signals, we further studied whether PI3K signaling was associated with the suppressive effect of TGF-β1. Like TGF-β1, the PI3K inhibitor LY294002 blocked iNOS expression and death in cultured microglial cells. Both TGF-β1 and LY294002 decreased the activation of caspases 3 and 11 and the mRNA expression of various kinds of inflammatory molecules caused by LPS. TGF-β1 was further found to decrease LPS-induced activation of PI3K and Akt. TGF-β1 and LY294002 suppressed LPS-induced p38 mitogen-activated kinase and c-Jun N-terminal kinase activity. In contrast, TGF-β1 and LY294002 enhanced LPS-induced NF-κB activity. Our data indicate that TGF-β1 protect normal or damaged brain tissue by repressing overactivation of microglial cells via inhibition of PI3K and its downstream signaling molecules.

Lymph nodes examined and margins obtained are important quality metrics in wedge resection. A high-quality wedge appears to confer a significant survival advantage over lower-quality wedge and stereotactic radiation. A margin-positive wedge appears to offer no benefit compared with radiation.

Unmanned aerial vehicles (UAVs), which are commonly known as drones, have proved to be useful not only on the battlefields where manned flight is considered too risky or difficult, but also in everyday life purposes such as surveillance, monitoring, rescue, unmanned cargo, aerial video, and photography. More advanced drones make use of global positioning system (GPS) receivers during the navigation and control loop which allows for smart GPS features of drone navigation. However, there are problems if the drones operate in heterogeneous areas with no GPS signal, so it is important to perform research into the development of UAVs with autonomous navigation and landing guidance using computer vision. In this research, we determined how to safely land a drone in the absence of GPS signals using our remote maker-based tracking algorithm based on the visible light camera sensor. The proposed method uses a unique marker designed as a tracking target during landing procedures. Experimental results show that our method significantly outperforms state-of-the-art object trackers in terms of both accuracy and processing time, and we perform test on an embedded system in various environments.

Lymph node assessment has improved since 2004 but varies by facility type and other characteristics. In our analysis removing at least 14 nodes was associated with more accurate staging.

Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.