• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.121-127
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• 2013

A vision based hand-gesture interface method for substituting a pointing device is proposed in this paper, which is used the ratio and size variation of Gesture Region. Proposed method uses the skin hue&saturation of the hand region from the HSI color model to extract the hand region effectively. This method can remove the non-hand region, and reduces the noise effect by the light source. Also, as the computation quantity is reduced by detecting not the static hand-shape recognition, but the ratio and size variation of hand-moving from the clipped hand region in real time, more response speed is guaranteed. In order to evaluate the performance of the our proposed method, after applying to the computerized self visual acuity testing system as a pointing device. As a result, the proposed method showed the average 86% gesture recognition ratio and 87% coordinate moving recognition ratio.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.26-29
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• 2003

최근 컴퓨팅 환경의 동향은 사람과 컴퓨터간의 좀더 자연스러운 인터페이스와 사용자의 눈에 보이지 않는 하드웨어의 계발이다. 디지털 데스크는 이 두 아이디어가 결합된 컴퓨팅 환경의 대표적인 예이다. 즉, 디지털 데스크에서 아무 장치도 하지 않은 사용자의 fingertip을 컴퓨터의 입력 장치로 사용하는 것이다. 본 논문은 디지털 데스크에서 사용자 fingertip의 이동경로를 추출하고, 추출된 이동경로로 symbolic gesture를 인식하는 방법을 제안한다. 제안된 방법은 Fingertip tracker, Gesture mode selector, 그리고 Symbolic gesture recognizer 세 개의 모듈로 구성된다. Fingertip tracker는 카메라로부터 입력되는 영상에서 사용자 fingertip의 이동경로를 추출하고, Gesture mode selector는 추출한 fingertip의 이동경로가 symbolic gesture인지를 구분한다. Symbolic gesture recognizer는 추출된 fingertip의 이동경로로 symbolic gesture를 인식한다. 이 방법을 문서교정 부호를 인식하여 전자문서를 교정하는 시스템에 적용해 본 결과 좋은 인식 결과를 얻을 수 있었다.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.545-559
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• 2020

In this paper, we propose a content creation framework that enables users without programming experience to easily create interactive media content that responds to user gestures. In the proposed framework, users define the gestures they use and the media effects that respond to them by numbers, and link them in a text-based configuration file. In the proposed framework, the interactive media content that responds to the user's gesture is linked with the dynamic projection mapping module to track the user's location and project the media effects onto the user. To reduce the processing speed and memory burden of the gesture recognition, the user's movement is expressed as a gray scale motion history image. We designed a convolutional neural network model for gesture recognition using motion history images as input data. The number of network layers and hyperparameters of the convolutional neural network model were determined through experiments that recognize five gestures, and applied to the proposed framework. In the gesture recognition experiment, we obtained a recognition accuracy of 97.96% and a processing speed of 12.04 FPS. In the experiment connected with the three media effects, we confirmed that the intended media effect was appropriately displayed in real-time according to the user's gesture.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.91-100
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• 2014

In this paper, we categorize existing human gesture recognition technologies to camera-based, additional hardware-based and frequency-based technologies. Then we describe several representative techniques for each of them, emphasizing their strengths and weaknesses. We define important performance issues for human gesture recognition technologies and analyze recent technologies according to the performance issues. Our analyses show that camera-based technologies are easy to use and have high accuracy, but they have limitations on recognition ranges and need additional costs for their devices. Additional hardware-based technologies are not limited by recognition ranges and not affected by light or noise, but they have the disadvantage that human must wear or carry additional devices and need additional costs for their devices. Finally, frequency-based technologies are not limited by recognition ranges, and they do not need additional devices. However, they have not commercialized yet, and their accuracies can be deteriorated by other frequencies and signals.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1106-1110
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• 2004

A human sign language is generally composed of both static and dynamic gestures. Each gesture is represented by a hand shape, its position, and hand movement (for a dynamic gesture). One of the problems found in automated sign language translation is on segmenting a hand movement that is part of a transitional movement from one hand gesture to another. This transitional gesture conveys no meaning, but serves as a connecting period between two consecutive gestures. Based on the observation that many dynamic gestures as appeared in Thai sign language dictionary are of quasi-periodic nature, a method was developed to differentiate between a (meaningful) dynamic gesture and a transitional movement. However, there are some meaningful dynamic gestures that are of non-periodic nature. Those gestures cannot be distinguished from a transitional movement by using the signal quasi-periodicity. This paper proposes a hybrid method using a combination of the periodicity-based gesture segmentation method with a HMM-based gesture classifier. The HMM classifier is used here to detect dynamic signs of non-periodic nature. Combined with the periodic-based gesture segmentation method, this hybrid scheme can be used to identify segments of a transitional movement. In addition, due to the use of quasi-periodic nature of many dynamic sign gestures, dimensionality of the HMM part of the proposed method is significantly reduced, resulting in computational saving as compared with a standard HMM-based method. Through experiment with real measurement, the proposed method's recognition performance is reported.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.483-492
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• 2012

Objective: The aim of this study is to suggest three-dimensional(3D) hand gesture taxonomy to organize the user's intention of his/her decisions on deriving a certain gesture systematically. Background: With advanced technologies of gesture recognition, various researchers have studied to focus on deriving intuitive gestures for commands from users. In most of the previous studies, the users' reasons for deriving a certain gesture for a command were only used as a reference to group various gestures. Method: A total of eleven studies which categorized gestures accompanied by speech were investigated. Also a case study with thirty participants was conducted to understand gesture-features which derived from the users specifically. Results: Through the literature review, a total of nine gesture-features were extracted. After conducting the case study, the nine gesture-features were narrowed down a total of seven gesture-features. Conclusion: Three-dimensional hand gesture taxonomy including a total of seven gesture-features was developed. Application: Three-dimensional hand gesture taxonomy might be used as a check list to understand the users' reasons.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.561-568
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• 2006

WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.585-588
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• 2008

This paper proposes hand gesture recognition using shape similarity method. For this, we require two steps which are aquisition of Hand area and similarity evaluation. First step is extracting hand area using YCbCr color spare. Then eliminate noise through filter and analyzing histogram. For doing this, we ran measure similarity of hand gesture by applying TSR after getting contour. Finally, we utilize shape similarity for recognizing of hand gesture.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.37-40
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• 2001

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 real-time processing. A key idea of the algorithm proposed in this paper is to apply morphological shape decomposition. The primitive elements extracted from a hand gesture have very important information including the directivity of the hand gestures. Based on this algorithm, we proposed the morphological hand-gesture recognition algorithm using feature vectors extracted from lines connecting the center points of a main-primitive element and sub-primitive elements. Through the experiments, we applied to the video contents browsing system with natural interactions and demonstrated the efficiency of this algorithm.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.553-556
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• 2005

We present a vision-based hand gesture recognition system for object manipulation in virtual space. Most conventional hand gesture recognition systems utilize a simpler method for hand detection such as background subtractions with assumed static observation conditions and those methods are not robust against camera motions, illumination changes, and so on. Therefore, we propose a statistical method to recognize and detect hand regions in images using geometrical structures. Also, Our hand tracking system employs multiple cameras to reduce occlusion problems and non-synchronous multiple observations enhance system scalability. Experimental results show the effectiveness of our method.