• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1289-1292
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• 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 Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.593-598
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• 2014

Hand gesture recognition based on vision is a challenging task in human-robot interaction. The sign language of finger spelling alphabets has been tested as a kind of hand gesture. In this paper, we test hand gesture recognition by detecting the contour shape and orientation of hand with visual image. The method has three stages, the first stage of finding hand component separated from the background image, the second stage of extracting the contour feature over the hand component and the last stage of comparing the feature with the reference features in the database. Here, finger spelling alphabets are used to verify the performance of our system and our method shows good performance to discriminate finger alphabets.

• ETRI Journal
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• v.34 no.3
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• pp.466-469
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• 2012

In this letter, we propose a dual autostereoscopic display platform employing a natural interaction method, which will be useful for sharing visual data with users. To provide 3D visualization of a model to users who collaborate with each other, a beamsplitter is used with a pair of autostereoscopic displays, providing a visual illusion of a floating 3D image. To interact with the virtual object, we track the user's hands with a depth camera. The gesture recognition technique we use operates without any initialization process, such as specific poses or gestures, and supports several commands to control virtual objects by gesture recognition. Experiment results show that our system performs well in visualizing 3D models in real-time and handling them under unconstrained conditions, such as complicated backgrounds or a user wearing short sleeves.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2930-2932
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• 2000

In this paper. a robust gesture recognition system is designed and implemented to explore the communication methods between human and computer. Hand gestures in the proposed approach are used to communicate with a computer for actions of a high degree of freedom. The user does not need to wear any cumbersome devices like cyber-gloves. No assumption is made on whether the user is wearing any ornaments and whether the user is using the left or right hand gestures. Image segmentation based upon the skin-color and a shape analysis based upon the invariant moments are combined. The features are extracted and used for input vectors to a radial basis function networks(RBFN). Our "Puppy" robot is employed as a testbed. Preliminary results on a set of gestures show recognition rates of about 87% on the a real-time implementation.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.11-23
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• 2016

Recent days, touch interaction interface is the most widely used interaction interface to communicate with digital devices. Because of its usability, touch technology is applied almost everywhere from watch to advertising boards and it is growing much bigger. However, this technology has a critical weakness. Normally, touch input device needs a contact surface with touch sensors embedded in it. Thus, touch interaction through general objects like books or documents are still unavailable. In this paper, a human-object interaction framework based on RGB-D camera is proposed to overcome those limitation. The proposed framework can deal with occluded situations like hovering the hand on top of the object and also moving objects by hand. In such situations object recognition algorithm and hand gesture algorithm may fail to recognize. However, our framework makes it possible to handle complicated circumstances without performance loss. The framework calculates the status of the object with fast and robust object recognition algorithm to determine whether it is an object or a human hand. Then, the hand gesture recognition algorithm controls the context of each object by gestures almost simultaneously.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.636-642
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• 2017

In this paper, We proposed a novel hand-gesture recognition algorithm using concentric-circle expanding and tracing. The proposed algorithm determines region of interest of hand image through preprocessing the original image acquired by web-camera and extracts the feature of hand gesture such as the number of stretched fingers, finger tips and finger bases, angle between the fingers which can be used as intuitive method for of human computer interaction. The proposed algorithm also reduces computational complexity compared with raster scan method through referencing only pixels of concentric-circles. The experimental result shows that the 9 hand gestures can be recognized with an average accuracy of 90.7% and an average algorithm execution time is 78ms. The algorithm is confirmed as a feasible way to a useful input method for virtual reality, augmented reality, mixed reality and perceptual interfaces of human computer interaction.

• Archives of design research
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• v.20 no.3 s.71
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• pp.257-268
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• 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 the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1725-1731
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• 2004

The use of gestures provides an attractive alternate to cumbersome interface devices for human-computer interaction. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. The most important issues in gesture recognition are the simplification of algorithm and the reduction of processing time. The mathematical morphology based on geometrical set theory is best used to perform the processing. A key idea of proposed algorithm in this paper is to apply morphological shape decomposition. The primitive elements extracted to a hand gesture include in very important information on the directivity of the hand gestures. Based on this characteristic, we proposed the morphological gesture recognition algorithm using feature vectors calculated to lines connecting the center points of a main-primitive element and sub-primitive elements. Through the experiment, we demonstrated the efficiency of proposed algorithm. Coupling natural interactions such as hand gesture with an appropriately designed interface is a valuable and powerful component in the building of TV switch navigating and video contents browsing system.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.847-850
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• 2008

On today, human is going to develop machine by using mutual communication to machine. Including vision - based HCI(Human Computer Interaction), the technique which to recognize finger and to track finger is important in HCI systems, in HCI systems. In order to divide finger, this paper uses more effectively dividing the technique using subtraction which is separation of background and foreground, as well as to divide finger from limited background and cluttered background. In order to divide finger, the finger is recognized to make "Template-Matching" by identified fingertip images. And, identified gestures be compared the tracked gesture after tracking recognized finger. In this paper, after obtaining interest area, not only using subtraction image and template-matching but to perform template-matching in the area. So, emphasis is placed on decreasing perform speed and reaction speed, and we propose technique which is more effectively recognizing gestures.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.57-64
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• 2014

Recently, many human-friendly communication methods have been studied for human-machine interface(HMI) without using any physical devices. One of them is the vision-based gesture recognition that this paper deals with. In this paper, we define some gestures for interaction with objects in a predefined virtual world, and propose an efficient method to recognize them. For preprocessing, we detect and track the both hands, and extract their silhouettes from the low-resolution hand images captured by a webcam. We modeled skin color by two Gaussian distributions in RGB color space and use blob-matching method to detect and track the hands. Applying the foodfill algorithm we extracted hand silhouettes and recognize the hand shapes of Thumb-Up, Palm and Cross by detecting and analyzing their modes. Then, with analyzing the context of hand movement, we recognized five predefined one-hand or both-hand gestures. Assuming that one main user shows up for accurate hand detection, the proposed gesture recognition method has been proved its efficiency and accuracy in many real-time demos.