• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.981-996
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• 2007

In this paper, we describe a method that can recognize gestures by obtaining motion features information with principal factor analysis from sequential gesture images. In the algorithm, firstly, a two dimensional silhouette region including human gesture is segmented and then geometric features are extracted from it. Here, global features information which is selected as some meaningful key feature effectively expressing gestures with principal factor analysis is used. Obtained motion history information representing time variation of gestures from extracted feature construct one gesture subspace. Finally, projected model feature value into the gesture space is transformed as specific state symbols by grouping algorithm to be use as input symbols of HMM and input gesture is recognized as one of the model gesture with high probability. Proposed method has achieved higher recognition rate than others using only shape information of human body as in an appearance-based method or extracting features intuitively from complicated gestures, because this algorithm constructs gesture models with feature factors that have high contribution rate using principal factor analysis.

• Journal of the Ergonomics Society of Korea
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• v.36 no.2
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• pp.109-121
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• 2017

Objective: This study aims to find suitable types and styles for gesture interaction as remote control on smart TVs. Background: Smart TV is being developed rapidly in the world, and gesture interaction has a wide range of research areas, especially based on vision techniques. However, most studies are focused on the gesture recognition technology. Also, not many previous studies of gestures types and styles on smart TVs were carried out. Therefore, it is necessary to check what users prefer in terms of gesture types and styles for each operation command. Method: We conducted an experiment to extract the target user manipulation commands required for smart TVs and select the corresponding gestures. To do this, we looked at gesture styles people use for every operation command, and checked whether there are any gesture styles they prefer over others. Through these results, this study was carried out with a process selecting smart TV operation commands and gestures. Results: Eighteen TV commands have been used in this study. With agreement level as a basis, we compared the six types of gestures and five styles of gestures for each command. As for gesture type, participants generally preferred a gesture of Path-Moving type. In the case of Pan and Scroll commands, the highest agreement level (1.00) of 18 commands was shown. As for gesture styles, the participants preferred a manipulative style in 11 commands (Next, Previous, Volume up, Volume down, Play, Stop, Zoom in, Zoom out, Pan, Rotate, Scroll). Conclusion: By conducting an analysis on user-preferred gestures, nine gesture commands are proposed for gesture control on smart TVs. Most participants preferred Path-Moving type and Manipulative style gestures based on the actual operations. Application: The results can be applied to a more advanced form of the gestures in the 3D environment, such as a study on VR. The method used in this study will be utilized in various domains.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.552-555
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• 2018

This study developed a gesture recognition method using Kinect sensor and proposed a fighting action control interface. To extract the pattern features of a gesture, it used a method of extracting them in consideration of a body rate based on the shoulders, rather than of absolute positions. Although the same gesture is made, the positional coordinates of each joint caught by Kinect sensor can be different depending on a length and direction of the arm. Therefore, this study applied principal component analysis in order for gesture modeling and analysis. The method helps to reduce the effects of data errors and bring about dimensional contraction effect. In addition, this study proposed a modified matching algorithm to reduce motion restrictions of gesture recognition system.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.222-225
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• 2000

Human frequently communicate non-linguistic information with gesture. So, we must develop efficient and fast gesture recognition algorithms for more natural human-computer interaction. However, it is difficult to recognize gesture automatically because human's body is three dimensional object with very complex structure. In this paper, we suggest a method which is able to detect key frames and frame changes, and to classify image sequence into some gesture groups. Gesture is classifiable according to moving part of body. First, we detect some frames that motion areas are changed abruptly and save those frames as key frames, and then use the frames to classify sequences. We symbolize each image of classified sequence using Principal Component Analysis(PCA) and clustering algorithm since it is better to use fewer components for representation of gestures. Symbols are used as the input symbols for the Hidden Markov Model(HMM) and recognized as a gesture with probability calculation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.22-31
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• 2004

This paper deals with the dynamic hand gesture recognition based on computer vision using neural networks. This paper proposes a global search method and a local search method to recognize the hand gesture. The global search recognizes a hand among the hand candidates through the entire image search, and the local search recognizes and tracks only the hand through the block search. Dynamic hand gesture recognition method is based on the skin-color and shape analysis with the invariant moment and direction information. Starting point and ending point of the dynamic hand gesture are obtained from hand shape. Experiments have been conducted for hand extraction, hand recognition and dynamic hand gesture recognition. Experimental results show the validity of the proposed method.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.499-505
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• 2012

Objective: Research on the transformation of interaction between mobile machines and users through analysis on current gesture interface technology development trend. Background: For smooth interaction between machines and users, interface technology has evolved from "command line" to "mouse", and now "touch" and "gesture recognition" have been researched and being used. In the future, the technology is destined to evolve into "multi-modal", the fusion of the visual and auditory senses and "3D multi-modal", where three dimensional virtual world and brain waves are being used. Method: Within the development of computer interface, which follows the evolution of mobile machines, actively researching gesture interface and related technologies' trend and development will be studied comprehensively. Through investigation based on gesture based information gathering techniques, they will be separated in four categories: sensor, touch, visual, and multi-modal gesture interfaces. Each category will be researched through technology trend and existing actual examples. Through this methods, the transformation of mobile machine and human interaction will be studied. Conclusion: Gesture based interface technology realizes intelligent communication skill on interaction relation ship between existing static machines and users. Thus, this technology is important element technology that will transform the interaction between a man and a machine more dynamic. Application: The result of this study may help to develop gesture interface design currently in use.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.802-816
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• 2010

To create gesture from a new text input is an important problem in computer games and virtual reality. Recently, there is increasing interest in gesture stylization to imitate the gestures of celebrities, such as announcer. However, no attempt has been made so far to stylize a gestures using emotion such as happiness and sadness. Previous researches have not focused on real-time algorithm. In this paper, we present a system to automatically make gesture animation from SMS text and stylize the gesture from emotion. A key feature of this system is a real-time algorithm to combine gestures with emotion. Because the system's platform is a mobile phone, we distribute much works on the server and client. Therefore, the system guarantees real-time performance of 15 or more frames per second. At first, we extract words to express feelings and its corresponding gesture from Disney video and model the gesture statistically. And then, we introduce the theory of Laban Movement Analysis to combine gesture and emotion. In order to evaluate our system, we analyze user survey responses.

• Journal of Korean Elementary Science Education
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• v.31 no.4
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• pp.450-462
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• 2012

Gestures are a common phenomenon of human communication. There exists little research concerned with the gestures in science education, and most researches of gestures have focused on individual gestures. However, learning occurs through sociocultural interactions with friends, family, teachers, and others in society. Hence, the purpose of this study was to investigate and identify the types and features of gestures which were made by elementary students to communicate with peers in science discourse. A group of six fourth-graders was observed in eight science discourses where they talked about ideas related to thermal concepts. The data was collected through interviews and questionnaires. The analysis of the data showed that students' gestures in science discourses could be classified into seven types: signal iconic gesture, illustrative iconic gesture, personal deictic gesture, object deictic gesture, beat gesture, emotional metaphoric gesture, and content metaphoric gesture. It was also found that these gestures had functions of repeating, supplementing, and replacing utterance to communicate with others. Students frequently expressed scientific terms metaphorically as everyday terms through their gestures. Gestures were shared, imitated, and transferred in the communication process, and students' gestures also made influence on other students' ideas through these processes.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.179-189
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• 2011

Gesture is one of the means for non-linguistic communication which can be expressed by human characters in animations. High drawing ability and profound knowledge about gestures are both required for animators to achieve efficient gesture expressions. However, diversified profound techniques are needed to master this knowledge which makes it very difficult for common animation drawers. In this paper, characteristics for each gesture are analyzed based on Laban Movement Analysis and gesture classification by making gesture packets. This research is of notable significance in providing animators with high efficient gesture expression techniques and developing automatic gesture-generating system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.371-377
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• 2008

In this paper, we present a framework for vision-based two-arm gesture recognition. To capture the motion information of the hands, we perform color-based tracking algorithm using adaptive kernel for each frame. And a feature selection algorithm is performed to classify the motion information into four different phrases. By using gesture phrase information, we build a gesture model which consists of a probability of the symbols and a symbol sequence which is learned from the longest common subsequence. Finally, we present a similarity measurement for two-arm gesture recognition by using the proposed gesture models. In the experimental results, we show the efficiency of the proposed feature selection method, and the simplicity and the robustness of the recognition algorithm.