• Korean Journal of Computational Design and Engineering
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• v.21 no.1
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• pp.20-30
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• 2016

Recently immersive virtual reality (VR) becomes popular due to the advanced development of I/O interfaces and related SWs for effectively constructing VR environments. In particular, natural and intuitive manipulation of 3D virtual objects is still considered as one of the most important user interaction issues. This paper presents a comparative study on the manipulation and interaction of 3D virtual objects using different interfaces and interactions in three VR environments. The comparative study includes both quantitative and qualitative aspects. Three different experimental setups are 1) typical desktop-based VR using mouse and keyboard, 2) hand gesture-supported desktop VR using a Leap Motion sensor, and 3) immersive VR by wearing an HMD with hand gesture interaction using a Leap Motion sensor. In the desktop VR with hand gestures, the Leap Motion sensor is put on the desk. On the other hand, in the immersive VR, the sensor is mounted on the HMD so that the user can manipulate virtual objects in the front of the HMD. For the quantitative analysis, a task completion time and success rate were measured. Experimental tasks require complex 3D transformation such as simultaneous 3D translation and 3D rotation. For the qualitative analysis, various factors relating to user experience such as ease of use, natural interaction, and stressfulness were evaluated. The qualitative and quantitative analyses show that the immersive VR with the natural hand gesture provides more intuitive and natural interactions, supports fast and effective performance on task completion, but causes stressful condition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.892-898
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• 2017

This paper describes the development of AVN(Audio Video Navigation) software for vehicle information analysis and gesture recognition. The module that examine the CAN(Controller Area Network) data of vehicle in the designed software analyzes the driving state. Using classified information, the AVN software converge vehicle information and hand gesture information. As the result, the derived data is used to match the service step and to perform the service. The designed AVN software was implemented in HW platform that common used in vehicles. And we confirmed the operation of vehicle analysing module and gesture recognition in a simulated environment that is similar with real world.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1225-1235
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• 2016

This paper presents a method for computer-assisted slide presentation using vision-based gesture recognition. The proposed method consists of a sequence of steps, first detecting a hand in the scene of projector beam, then estimating the smooth trajectory of a hand or a pointing finger using Kalman Filter, and finally interfacing to an application system. Additional slide navigation control includes moving back and forth the pages of the presentation. The proposed method is to help speakers for an effective presentation with natural improved interaction with the computer. In particular, the proposed method of using finger pointing is believed to be more effective than using a laser pointer since the hand, the pointing or finger are more visible and thus can better grab the attention of the audience.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.13-20
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• 2015

In this paper, we propose an algorithm that can recognize not only the number of stretched fingers but also determination of attached fingers for extracting features required for hand gesture recognition. The proposed algorithm detects the hand area in the input image by the skin color range filter based on a color model and labeling, and then recognizes various hand gestures by extracting the number of stretched fingers and determination of attached fingers using curvature information extracted from outlines and feature points. Experiment results show that the recognition rate and the frame rate are similar to those of the conventional algorithm, but the number of gesture cases that can be defined by the extracted characteristics is about four times higher than the conventional algorithm, so that the proposed algorithm can recognize more various gestures.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.214-216
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• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.46-51
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• 2007

This paper is to present gesture analysis for human-robot interaction. Understanding human emotions through gesture is one of the necessary skills fo the computers to interact intelligently with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. For efficient operation we used recognizing a gesture with HMM(Hidden Markov Model). We constructed a large gesture database, with which we verified our method. As a result, our method is successfully included and operated in a mobile system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.587-593
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• 2006

This paper is to present gesture analysis for human-robot interaction. Understanding human emotions through gesture is one of the necessary skills for the computers to interact intelligently with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. For efficient operation we used recognizing a gesture with HMM(Hidden Markov Model). We constructed a large gesture database, with which we verified our method. As a result, our method is successfully included and operated in a mobile system.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.153-156
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• 2005

Skin region detection in images is an important process in many computer vision applications targeting humans such as hand gesture recognition and face identification. It usually starts at a pixel-level, and involves a pre-process of color spae transformation followed by a classification process. A color space transformation is assumed to increase separability between skin classes and other classes, to increase similarity among different skin tones, and to bring a robust performance under varying imaging conditions, without any complicated analysis. In this paper, we examine if the color space transformation actually brings those benefits to the problem of skin region detection on a set of human hand images with different postures, backgrounds, people, and illuminations. Our experimental results indicate that color space transfomation affects the skin detection performance. Although the performance depends on camera and surround conditions, normalized [R, G, B] color space may be a good choice in general.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.9
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• pp.68-76
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• 1997

This paper presents a system which recognizes dynamic hand gestures for virtual reality(VR). A dynamic hand gesture is a method of communication for a computer and human who uses gestures, especially both hands and fingers. Since the human hands and fingers are not the same in physical dimension, the same form of a gestrue produced by two persons with their hands may not have the same numerical values which are obtained through electronic sensors. In this paper, we apply a fuzzy min-max neural network and feature analysis method using fuzzy logic for on-line pattern recognition.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.2
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• pp.61-70
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• 1997

Human-hand gestures have been used a means of communication among people for a long time, being interpreted as streams of tokens for a language. The signed language is a method of communication for hearing impaired person. Articulated gestures and postures of hands and fingers are commonly used for the signed language. This paper presents a system which recognizes the korean sign language (KSL) and translates the recognition results into a normal korean text and sound. A pair of data-gloves are used a sthe sensing device for detecting motions of hands and fingers. In this paper, we propose a dynamic gesture recognition mehtod by employing a fuzzy feature analysis method for efficient classification of hand motions, and applying a fuzzy min-max neural network to on-line pattern recognition.