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The organic light emitting diode (OLED) display has been widely adopted to various multimedia devices with superior performance in terms of image quality and power efficiency. However, the luminance degradation of the OLEDs, called burn-in, is still one of the major problems. This paper presents a novel method of detecting the burn-in potential region (BPR) to alleviate the luminance degradation. First, in order to extract the burn-in potential pixels (BPPs) which deteriorate the uniformity of the display, we calculate the remaining lifetime of OLED of each pixel. Then, the BPRs are detected by the level set based image segmentation using the BPPs as the seed points. The experimental results demonstrate that the proposed method detects BPRs with superior effectiveness compared with other alternative methods.

We propose an intelligent vision-based Automated Guided Vehicle (AGV) system using fiduciary markers. In this paper, we explore a low-cost, efficient vehicle guiding method using a consumer grade web camera and fiduciary markers. In the proposed method, the system uses fiduciary markers with a capital letter or triangle indicating direction in it. The markers are very easy to produce, manipulate, and maintain. The marker information is used to guide a vehicle. We use hue and saturation values in the image to extract marker candidates. When the known size fiduciary marker is detected by using a bird's eye view and Hough transform, the positional relation between the marker and the vehicle can be calculated. To recognize the character in the marker, a distance transform is used. The probability of feature matching was calculated by using a distance transform, and a feature having high probability is selected as a captured marker. Four directional signals and 10 alphabet features are defined and used as markers. A 98.87% recognition rate was achieved in the testing phase. The experimental results with the fiduciary marker show that the proposed method is a solution for an indoor AGV system.

In this paper, we describe a vision sensor-based driving algorithm for indoor automatic guided vehicles (AGVs) that facilitates a path tracking task using two mono cameras for navigation. One camera is mounted on vehicle to observe the environment and to detect markers in front of the vehicle. The other camera is attached so the view is perpendicular to the floor, which compensates for the distance between the wheels and markers. The angle and distance from the center of the two wheels to the center of marker are also obtained using these two cameras. We propose five movement patterns for AGVs to guarantee smooth performance during path tracking: starting, moving straight, pre-turning, left/right turning, and stopping. This driving algorithm based on two vision sensors gives greater flexibility to AGVs, including easy layout change, autonomy, and even economy. The algorithm was validated in an experiment using a two-wheeled mobile robot.

This paper presents human action recognition at night. Because it is difficult to detect object applying only color or infrared camera at night, we use infrared thermal camera. And temporal templates, HoG and SVM are employed. Our experimental results show that the system achieves good performance.

In working environments with large manipulators, accidental collisions can cause severe personal injuries and can seriously damage manipulators, necessitating the development of an emergency stop algorithm to prevent such occurrences. In this paper, we propose an emergency stop system for the efficient and safe operation of a manipulator by applying an intelligent emergency stop algorithm. Our proposed intelligent algorithm considers the direction of motion of the manipulator. In addition, using a new regression method, the algorithm includes a decision step that determines whether a detected object is a collision-causing obstacle or a part of the manipulator. We apply our emergency stop system to a two-link manipulator and assess the performance of our intelligent emergency stop algorithm as compared with other models.

Central pattern generator (CPG) is a kind of neural circuit which can be observed in many animals showing rhythmic patterns of actions. The CPG neural circuit can produce complex rhythmic patterns by receiving only simple signals from the brain. Generally, the CPG neural models can be applied to solve robotic problems, or to understand the underlying neural mechanism for rhythmic animal behaviours. In this paper, we focus on how a small number of neurons generate the variable frequencies and phase of motor actions, and inspect what is the capacity of a varying number of neurons as a CPG model. The performance measure consists of frequency variability, input/output response rate, and phase shift. We have used evolutionary computation to measure the best performance for each number of CPG neurons ranging from two to eight neurons, and the result shows that four neurons or more can easily generate variable frequencies and anti-phase difference for left and right motor actions.