A horseback riding simulator is a robotic machine that simulates the motion of horseback riding. In this paper, we present an interaction control system for a horseback riding simulator. The proposed system provides a postural correction function suited to the user based on their historical log and specialized posture coaching data. The system has adopted certain schemes for posture detection and recognition of the identified user as a way to recommend customized exercise modes. Our experiments show that including these techniques will help users maintain good posture while riding.

IT technologies have started to be applied to many fields such as sports science, robotics or BT. Especially, object detection and recognition using vision sensor has intensively studied due to diverse application fields. But, various illumination condition, pose or time progress have put object detection and recognition to challenge task in the real world. In this paper, face detection and recognition using vision sensor applied to sports simulator which has been proposed. Face detection has been processed to identify straight in the face that has used to detect head pose of riders. Face recognition has used to identify user, who has tried to take horse riding simulator. Gabor wavelet and face graph has used to recognize low quality face image, which has acquired under poor illumination environment and movement of simulator. We have simulated on FERET and ETRI database. ETRI database has acquired on horse riding simulator under poor illumination condition. The accuracy of 91% face recognition rate on FERET and encouraging result of 82% recognition rate on ETRI DB have been obtained.

This paper proposes user segmentation algorithm using moving edge detection to provide customized coaching service to users in advanced sports simulator like horse riding. To give this service in the simulator, exact user segmentation is very important. If the segmenting user from its background became better, we will get a better pose recognition results. In this paper, some of the well-known object segmentation methods to be applied to sports simulator have been investigated. Also, a moving object detection algorithm for recognizing user's poses which is a novel innovation to assure high recognition reliability in sports simulator has been proposed.

Recently, some 2D based algorithms were proposed for image classification. In this paper, we propose two-dimensional logistic regression for two-class problem. This method has an advantage of memory saving and better performance than other 2D based approaches. The experimental results show two dimensional logistic regression is superior to one dimensional logistic regression and other popular two-dimensional methods.

This paper describes a new robot photographer system which can interact with people. The goal of this research is to make the system act like a human photographer. This system is based on a mobile robot having capabilities of wireless communication and stereo vision. It recognizes waving hands of people, moves toward them, and takes pictures with designated compositions. The pictures are transmitted to personal computers over the wireless network. In comparison with previous researches, the most unique things of this system are human interaction and user preference. To realize these properties, this new robot photographer system is considered optical property of lens and applied interesting vision algorithm which was never tried before for previous robot photographer systems

In this paper, an algorithm for human head detection over a distance exceeding 2.5 m between a camera and an object is described. This algorithm is used for the control of a robot, which has the additional limits of a moving camera, moving objects, various face orientations, and unfixed illuminations. With these circumstances, only the assumption that human head and body contours have an omega (Omega) shape is made. In order to separate the background from these omega shapes, the three basic features of gray, color and edge are used, and combined. The skin color cue is very useful when the image stream is frontal and has large face regions, and additionally has no background objects similar to the skin color. The gray cue is also important when captured faces have a lower gray level than background objects. The edge cue is helpful when captured background objects have similar gray levels and colors to those of a head, but can be discriminated by edges. Since these three methods have roughly orthogonal failure results, they serve to complement each other. The next step is a splitting method between the head and body region using the proposed method. The final step is an ellipse fitting and a head region verification algorithm. The results of this algorithm provide robustness for head rotation, illumination changing, and variable head sizes. Furthermore, it is possible to carry out real time processing

The ability to recognize people is positively necessary for interaction between robots and people. In this paper, we introduce intelligent security system that is able to recognize, track and remember user's face. System uses moving camera that has pan/tilt and zoom function like robot's eyes. In order to construct system, we propose the face identification method based on combining multiple features and applying multiple matching algorithms. Also, we address the benefits of multiple instances and show that identification ability can be improved by using proper combination strategy

The purpose of the research presented is to segment a text image printed in both Korean and English into character images, utilizing the structure information in Korean and English characters, and using a Korean, English and mixed language character recognizer. The image cannot be separated by only using the width and height of a character because those of an English character are not constant, contrary to those of a Korean character. Therefore we first classify the image into Korean or English using the structure information in Korean and English characters. If it is determined as a Korean character, we segment it with the average width of Korean characters in the text lines. If it is determined as an English character, we segment it using a classical method to segment touching alphanumeric characters. If it cannot be determined, we find possible cut points using a vertical histogram and use the mixed language recognizer to determine the right cut point. Since our method first classifies a block into Korean or English, it can be run faster than the traditional method that cannot identify the language. Each classified block can be segmented more accurately because more specific knowledge about Korean and English characters can be applied.