Recently, we experienced significant advancement in intelligent service robots. The remarkable features of an intelligent robot include tracking and identification of person using biometric features. The human-robot interaction is very important because it is one of the final goals of an intelligent service robot. Many researches are concentrating in two fields. One is self navigation of a mobile robot and the other is human-robot interaction in natural environment. In this paper we will present an effective person identification method for HRI (Human Robot Interaction) using two different types of expert systems. However, most of mobile robots run under uncontrolled and complicated environment. It means that face and speech information can't be guaranteed under varying conditions, such as lighting, noisy sound, orientation of a robot. According to a value of illumination and signal to noise ratio around mobile a robot, our proposed fuzzy rule make a reasonable person identification result. Two embedded HMM (Hidden Marhov Model) are used for each visual and audio modality to identify person. The performance of our proposed system and experimental results are compared with single modality identification and simply mixed method of two modality.
Recently, surveillance systems gain more attraction than simple CCTV systems, especially for complicated security environment. The major purpose of the proposed system is to monitor and track intruders. More specifically, accurate identification of each intruder is more important than simply recording what they are doing. Most existing surveillance systems simply keep recording the fixed viewing area, and some others adopt the tracking technique for wider coverage.
Although panning and tilting the camera can extend the viewing area, only a few automatic zoom control techniques for acquiring the optimum ROI has been proposed. This paper describes a system for tracking multiple faces from input video sequences using facial convex hull-based facial segmentation and robust hausdorff distance. The proposed algorithm adapts skin color reference map in the YCbCr color space and hair color reference map in the RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide experimental result to demonstrate the performance of the proposed tracking algorithm, which efficiently tracks rotating, and zooming faces as well as multiple faces in video sequences obtained from at CCD camera.
This paper represents a new method to monitor the soldering state of a ball grid array by newly designed digital tomosynthesis system. Firstly, a new digital tomosynthesis (DTS) system, called object-detector synchronous rotation (ODSR), is suggested and designed to acquire images for the soldering state of a ball grid array. Secondary, the shape distortion of DTS images generated by an image intensifier is modeled. And a new synthesis algorithm, which overcomes the limitations of the existing synthesis algorithms is suggested to improve the sharpness of the synthesized image. Also an artifact analysis of the DTS system is performed. Thirdly, the experiment to obtain the cross-sectional images of ball grid arrays is accomplished by the ODSR system.