• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.126-131
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• 2017

In this paper, we propose a method to extract meaningful motion among various kinds of hand gestures on giving commands to robots using hand gestures. On giving a command to the robot, the hand gestures of people can be divided into a preparation one, a main one, and a finishing one. The main motion is a meaningful one for transmitting a command to the robot in this process, and the other operation is a meaningless auxiliary operation to do the main motion. Therefore, it is necessary to extract only the main motion from the continuous hand gestures. In addition, people can move their hands unconsciously. These actions must also be judged by the robot with meaningless ones. In this study, we extract human skeleton data from a depth image obtained by using a Kinect v2 sensor and extract location data of hands data from them. By using the Kalman filter, we track the location of the hand and distinguish whether hand motion is meaningful or meaningless to recognize the hand gesture by using the hidden markov model.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.527-536
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• 2004

TheReviser is a digital document revision application on a projection display, which allows us to interact a digital document with the same gestures used for paper documents revision. To enable these interactions, TheReviser should detect foreground objects such as hands or pens on a projection display, and should spot and recognize gesture commands from continuous movements of a user. To detect foreground objects from a complex background in various lighting conditions, we perform geometry and color calibration between a captured image and a frame buffer image. TheReviser uses an HMM-based gesture recognition method Experimental results show that the proposed application recognizes user's gestures on average 93.22% in test gesture sequences.

• 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.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1320-1328
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• 2016

In this paper, we propose a novel security system using hand gesture recognition. Proposed system does not create a password as numbers, but instead, it creates unique yet simple pattern created by user's hand movement. Because of the fact that individuals have different range of hand movement, speed, direction, and size while drawing a pattern with their hands, the system will be able to accurately recognize only the authorized user. To evaluate the performance of our system, various patterns were tested and the test showed a satisfying result.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.116-119
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• 2019

User interface/experience and realistic game manipulation play an important role in virtual reality first-person-shooting game. This paper presents an intuitive hands-free interface of gaming interaction scheme for FPS based on user's gesture recognition and VR controller. We focus on conventional interface of VR FPS interaction, and design the player interaction wearing head mounted display with two motion controllers; leap motion to handle low-level physics interaction and VIVE tracker to control movement of the player joints in the VR world. The FPS prototype system shows that the design interface helps to enjoy playing immersive FPS and gives players a new gaming experience.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.759-768
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• 2005

This paper describes an algorithm that can recognize gestures constructing subspace gesture symbols with hybrid feature information. The previous popular methods based on geometric feature and appearance have resulted in ambiguous output in case of recognizing between similar gesture because they use just the Position information of the hands, feet or bodily shape features. However, our proposed method can classify not only recognition of motion but also similar gestures by the partial feature information presenting which parts of body move and the global feature information including 2-dimensional bodily motion. And this method which is a simple and robust recognition algorithm can be applied in various application such surveillance system and intelligent interface systems.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.328-336
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• 2012

In this paper, we propose a robust fingertip extraction and extended Continuously Adaptive Mean Shift (CAMSHIFT) based robust hand gesture recognition for natural human-like HRI (Human-Robot Interaction). Firstly, for efficient and rapid hand detection, the hand candidate regions are segmented by the combination with robust $YC_bC_r$ skin color model and haar-like features based adaboost. Using the extracted hand candidate regions, we estimate the palm region and fingertip position from distance transformation based voting and geometrical feature of hands. From the hand orientation and palm center position, we find the optimal fingertip position and its orientation. Then using extended CAMSHIFT, we reliably track the 2D hand gesture trajectory with extracted fingertip. Finally, we applied the conditional density propagation (CONDENSATION) to recognize the pre-defined temporal motion trajectories. Experimental results show that the proposed algorithm not only rapidly extracts the hand region with accurately extracted fingertip and its angle but also robustly tracks the hand under different illumination, size and rotation conditions. Using these results, we successfully recognize the multiple hand gestures.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.27-34
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• 2015

This paper proposes a user interface for enhancing immersiveness and usability by combining hologram and haptic device with common Leap Motion. While Leap Motion delivers physical motion of user hand to control virtual environment, it is limited to handle virtual hands on screen and interact with virtual environment in one way. In our system, hologram is coupled with Leap Motion to improve user immersiveness by arranging real and virtual hands in the same place. Moreover, we provide a interaction prototype of sense by designing a haptic device to convey touch sense in virtual environment to user's hand.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.265-279
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• 2008

The idea of using hand gestures for human-computer interaction is not new and has been studied intensively during the last dorado with a significant amount of qualitative progress that, however, has been short of our expectations. This paper describes a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on the image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of skin extraction and modeling, and motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to a model. In an experiment with ten isolated gestures, we obtained the recognition rate upwards of 99.59% with cross validation. The proposed model and the related approach are believed to have a strong potential for successful applications to other related problems such as sign languages.