• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.4
• /
• pp.935-942
• /
• 2011

This study aims to design and implement the finger gesture recognizing system that automatically recognizes finger gestures input through a camera and controls the computer. Common CCD cameras were redesigned as infrared light cameras to acquire the images. The recorded images go through the pre-process to find the hand features, the finger gestures are read accordingly, and an event takes place for the follow-up mouse controlling and presentation, and finally the way to control computers is suggested. The finger gesture recognizing system presented in this study has been verified as the next-generation interface to replace the mouse and keyboard for the future information-based units.

• Journal of KIISE:Software and Applications
• /
• v.36 no.12
• /
• pp.1028-1033
• /
• 2009

This paper presents a real-time hand gesture recognition approach for controlling a computer. We define hand gestures as continuous hand postures and their movements for easy expression of various gestures and propose a Two-layered Bayesian Network (TBN) to recognize those gestures. The proposed method can compensate an incorrectly recognized hand posture and its location via the preceding and following information. In order to vertify the usefulness of the proposed method, we implemented a Virtual Mouse interface, the gesture-based interface of a physical mouse device. In experiments, the proposed method showed a recognition rate of 94.8% and 88.1% for a simple and cluttered background, respectively. This outperforms the previous HMM-based method, which had results of 92.4% and 83.3%, respectively, under the same conditions.

• Archives of design research
• /
• v.20 no.3 s.71
• /
• pp.257-268
• /
• 2007

The development of Human Computer Interface has been relied on the development of technology. Mice and keyboards are the most popular HCI devices for personal computing. However, device-based interfaces are quite different from human to human interaction and very artificial. To develop more intuitive interfaces which mimic human to human interface has been a major research topic among HCI researchers and engineers. Also, technology in the TV industry has rapidly developed and the market penetration rate for big size screen TVs has increased rapidly. The HDTV and digital TV broadcasting are being tested. These TV environment changes require changes of Human to TV interface. A gesture recognition-based interface with a computer vision system can replace the remote control-based interface because of its immediacy and intuitiveness. This research focuses on how people use their hands or arms for command gestures. A set of gestures are sampled to control TV set up by focus group interviews and surveys. The result of this paper can be used as a reference to design a computer vision based TV interface.

• Journal of Korean Institute of Industrial Engineers
• /
• v.38 no.3
• /
• pp.182-190
• /
• 2012

With the progress of both wire and wireless home networking technology, various projects on smart home have been carried out in the world (Harper, 2003), and at the same time, new approaches to interact with smart home systems efficiently and effectively have also been investigated. A gesture-based interface is one of these approaches. Especially with advance of gesture recognition technologies, a variety of research studies on gesture interactions with the functions of IT devices have been conducted. However, there are few research studies which suggested and investigated the use of gestures for controlling smart home appliances. In this research the gestures for selected smart home appliances are suggested based on a user centered approach. A total of thirty-eight functions were selected, and a total of thirty participants generated gestures for each function. Based on the Nielsen (2004), Lee et al. (2010) and Kuhnel et al. (2011), the gesture with the highest frequency for each function (Top gesture) has been suggested and investigated.

• Journal of Korea Game Society
• /
• v.9 no.5
• /
• pp.105-115
• /
• 2009

This article proposes a design and implementation methodology of a gesture-based interface for augmented reality games. The topic of gesture-based augmented reality games is a promising area in the immersive future games using human body motions. However, due to the instability of the current motion recognition technologies, most previous development processes have introduced many ad hoc methods to handle the shortcomings and, hence, the game architectures have become highly irregular and inefficient This article proposes an efficient development methodology for gesture-based augmented reality games through prototyping a table tennis game with a gesture interface. We also verify the applicability of the prototyping mechanism by implementing and demonstrating the augmented reality table tennis game. In the experiments, the implemented prototype has stably tracked real rackets to allow fast movements and interactions without delay.

• Journal of Korea Multimedia Society
• /
• v.17 no.11
• /
• pp.1263-1269
• /
• 2014

As rapid development of new hardware control interface technology, new concepts have been being proposed and emerged. In this paper, a new approach based on leap motion system is proposed. While we employ a position information from sensor, the hand gesture recognition is suggested with the pre-defined patterns. To do this, we design a recognition algorithm with hand gesture and finger patterns. We apply the proposed scheme to 3-dimensional avatar controling and editing software tool for making animation in the cyber space as a representative application. This proposed algorithm can be used to control computer systems in medical treatment, game, education and other various areas.

• Journal of Korea Multimedia Society
• /
• v.6 no.3
• /
• pp.469-480
• /
• 2003

The accurate user- authentication technology is being raised as one of the most important in this current society, which is, so called, information society. Most authentication technology is used to identify users by using the special characteristics of users. This paper has established an e-commerce shopping mall based on conventional e-commerce systems. It also suggested and established the user authentication interface that uses the mouse gesture, which is the new authentication of what users have. The user authentication interface using the mouse gesture generates the status of recognition directly on the screen by comparing the stored pattern values with the unique pattern values that users entered. When users purchase products through the shopping mall and enter their another signature information together with payment information, security can be more increased. Experimental results show that our mouse gesture interface may be useful to provide more security to e-commerce server.

• Smart Media Journal
• /
• v.1 no.2
• /
• pp.40-46
• /
• 2012

In this paper, we describe interactive contents which is used the result of the inputted interface recognizing vision-based body gesture. Because the content uses the imp which is the common culture as the subject in Asia, we can enjoy it with culture familiarity. And also since the player can use their own gesture to fight with the imp in the game, they are naturally absorbed in the game. And the users can choose the multiple endings of the contents in the end of the scenario. In the part of the gesture recognition, KINECT is used to obtain the three-dimensional coordinates of each joint of the limb to capture the static pose of the actions. The vision-based 3D human pose recognition technology is used to method for convey human gesture in HCI(Human-Computer Interaction). 2D pose model based recognition method recognizes simple 2D human pose in particular environment On the other hand, 3D pose model which describes 3D human body skeletal structure can recognize more complex 3D pose than 2D pose model in because it can use joint angle and shape information of body part Because gestures can be presented through sequential static poses, we recognize the gestures which are configured poses by using HMM In this paper, we describe the interactive content which is used as input interface by using gesture recognition result. So, we can control the contents using only user's gestures naturally. And we intended to improve the immersion and the interest by using the imp who is used real-time interaction with user.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1289-1292
• /
• 1998

HCI(human computer interface) technologies have been often implemented using mouse, keyboard and joystick. Because mouse and keyboard are used only in limited situation, More natural HCI methods such as speech based method and gesture based method recently attract wide attention. In this paper, we present multi-modal input system to control Windows system for practical use of multi-media computer. Our multi-modal input system consists of three parts. First one is virtual-hand mouse part. This part is to replace mouse control with a set of gestures. Second one is Windows control system using speech recognition. Third one is Windows control system using gesture recognition. We introduce neural network and HMM methods to recognize speeches and gestures. The results of three parts interface directly to CPU and through Windows.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.5
• /
• pp.621-625
• /
• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.