• 한국정보과학회논문지:소프트웨어및응용
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• 제26권1호
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• pp.157-157
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• 1999

Researches on the gesture recognition have become a very interesting topic in the computer vision area, Gesture recognition from visual images has a number of potential applicationssuch as HCI (Human Computer Interaction), VR(Virtual Reality), machine vision. To overcome thetechnical barriers in visual processing, conventional approaches have employed cumbersome devicessuch as datagloves or color marked gloves. In this research, we capture gesture images without usingexternal devices and generate a gesture trajectery composed of point-tokens. The trajectory Is spottedusing phase-based velocity constraints and recognized using the discrete left-right HMM. Inputvectors to the HMM are obtained by using the LBG clustering algorithm on a polar-coordinate spacewhere point-tokens on the Cartesian space .are converted. A gesture vocabulary is composed oftwenty-two dynamic hand gestures for editing drawing elements. In our experiment, one hundred dataper gesture are collected from twenty persons, Fifty data are used for training and another fifty datafor recognition experiment. The recognition result shows about 95% recognition rate and also thepossibility that these results can be applied to several potential systems operated by gestures. Thedeveloped system is running in real time for editing basic graphic primitives in the hardwareenvironments of a Pentium-pro (200 MHz), a Matrox Meteor graphic board and a CCD camera, anda Window95 and Visual C++ software environment.

• 한국멀티미디어학회논문지
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• 제14권7호
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• pp.866-871
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• 2011

In this paper, an improved method of hand detecting and hand gesture recognition is proposed, it can be applied in different illumination condition and complex background. We use Adaptive Skin Threshold (AST) to detect the areas of hand. Then the result of hand detection is used to hand recognition through the improved HMM algorithm. At last, we design a simple program using the result of hand recognition for recognizing "stone, scissors, cloth" these three kinds of hand gesture. Experimental results had proved that the hand and gesture can be detected and recognized with high average recognition rate (92.41%) and better than some other methods such as syntactical analysis, neural based approach by using our approach.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.129-138
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• 2022

With the increasing reliance of computing systems in our everyday life, there is always a constant need to improve the ways users can interact with such systems in a more natural, effective, and convenient way. In the initial computing revolution, the interaction between the humans and machines have been limited. The machines were not necessarily meant to be intelligent. This begged for the need to develop systems that could automatically identify and interpret our actions. Automatic gesture recognition is one of the popular methods users can control systems with their gestures. This includes various kinds of tracking including the whole body, hands, head, face, etc. We also touch upon a different line of work including Brain-Computer Interface (BCI), Electromyography (EMG) as potential additions to the gesture recognition regime. In this work, we present an overview of several applications of automated gesture recognition systems and a brief look at the popular methods employed.

• 한국멀티미디어학회논문지
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• 제13권7호
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• pp.1000-1015
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• 2010

본 논문에서는 손동작 인식을 위한 새로운 방법을 제안한다. 손 추출을 위한 방법으로는 피부색과 boundary energy 정보를 이용하고 moment method로 손바닥의 중심을 구하게 된다. 손동작 인식은 두 단계로 나눌 수 있다. 첫 번째 단계는 손 형상 인식으로 병렬 신경망을 이용하였다. 손 형상의 패턴을 추출하기 위해서 fitting ellipses method를 이용하였다. fitting ellipses method는 추출된 손 영역을 12개의 타원형으로 분류하고 타원 외곽선의 흰 픽셀 비율을 계산한다. 패턴은 12개의 입력 노드로 신경망에 입력되고 4개의 출력 노드로 출력되는데 각 출력 노드는 0~1사이의 값을 갖게 된다. 손 형상은 4개의 출력 노드의 구성으로 나타낼 수 있다. 두 번째 단계는 손동작 추적과 인식이다. 손동작 추적과 인식을 위해서는 손동작의 위치 정보를 예측 할 수 있는 Kalman Filter를 이용하였다. 실험은 Windows XP상에서 수행되었고 제안한 알고리즘의 효율성을 평가하였다. 손 형상을 인식하기 위해서 300개의 이미지를 인식기에 훈련시키고 200개의 이미지를 실험에 사용하였다. 194개 이상의 이미지가 정확하게 인식 되었다. 그리고 손동작 추적 인식을 실험하기 위해서 1200번의 손동작(각 동작은 400번)을 사용하였고 그 중 1002번의 손동작이 정확하게 인식 되었다.이러한 결과는 제안된 방법이 손 영역을 추출하고 손 동작을 인식하는데 유용함을 확인 할 수 있었다.

• 한국멀티미디어학회논문지
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• 제14권4호
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• pp.504-516
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• 2011

In this paper, we will present a method to detect human hand and recognize hand gesture. For detecting the hand region, we use the feature of human skin color and hand feature (with boundary complexity) to detect the hand region from the input image; and use algorithm of optical flow to track the hand movement. Hand gesture recognition is composed of two parts: 1. Posture recognition and 2. Motion recognition, for describing the hand posture feature, we employ the Fourier descriptor method because it's rotation invariant. And we employ PCA method to extract the feature among gesture frames sequences. The HMM method will finally be used to recognize these feature to make a final decision of a hand gesture. Through the experiment, we can see that our proposed method can achieve 99% recognition rate at environment with simple background and no face region together, and reduce to 89.5% at the environment with complex background and with face region. These results can illustrate that the proposed algorithm can be applied as a production.

• 한국정보통신학회논문지
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• 제26권3호
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• pp.396-402
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• 2022

영상 미디어를 위한 사용자 인터랙션은 다양한 형태로 개발되고 있으며, 특히, 인간의 제스처를 활용한 인터랙션이 활발히 연구되고 있다. 그 중에, 손 제스처 인식의 경우 3D Hand Model을 기반으로 실감 미디어 분야에서 휴먼 인터페이스로 활용되고 있다. 손 제스처 인식을 기반으로 한 인터페이스의 활용은 사용자가 미디어 매체에 보다 쉽고 편리하게 접근할 수 있도록 도와준다. 이러한 손 제스처 인식을 활용한 사용자 인터랙션은 컴퓨터 환경 제약 없이 빠르고 정확한 손 제스처 인식 기술을 적용하여 영상을 감상할 수 있어야 한다. 본 논문은 오픈 소스인 미디어 파이프 프레임워크와 머신러닝의 k-NN(K-Nearest Neighbor)을 활용하여 빠르고 정확한 사용자 손 제스처 인식 알고리즘을 제안한다. 그리고 컴퓨터 환경 제약을 최소화하기 위하여 인터넷 서비스가 가능한 웹 서비스 환경 및 가상 환경인 도커 컨테이너를 활용하여 사용자 손 제스처 인식 기반의 입체 영상 제어 시스템을 설계하고 구현한다.

• 한국멀티미디어학회논문지
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• 제15권4호
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• pp.417-424
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• 2012

In this paper, we propose an automatic system that recognizes dynamic human gestures activity, including Arabic numbers from 0 to 9. We assume the gesture trajectory is almost in a plane that called principal gesture plane, then the Least Squares Method is used to estimate the plane and project the 3-D trajectory model onto the principal. An improved FNN model combined with HMM is proposed for dynamic gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network. The proposed algorithm shows that satisfactory performance and high recognition rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.787-806
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• 2016

Traditional radio-based gesture recognition approaches usually require the target to carry a device (e.g., an EMG sensor or an accelerometer sensor). However, such requirement cannot be satisfied in many applications. For example, in smart home, users want to control the light on/off by some specific hand gesture, without finding and pressing the button especially in dark area. They will not carry any device in this scenario. To overcome this drawback, in this paper, we propose three algorithms able to recognize the target gesture (mainly the human hand gesture) without carrying any device, based on just Radio Signal Strength Indicator (RSSI). Our platform utilizes only 6 telosB sensor nodes with a very easy deployment. Experiment results show that the successful recognition radio can reach around 80% in our system.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (C)
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• pp.263-266
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• 2007

We introduce a vision-based hand gesture recognition fer understanding musical time and patterns without extra special devices. We suggest a simple and reliable vision-based hand gesture recognition having two features First, the motion-direction code is proposed, which is a quantized code for motion directions. Second, the conducting feature point (CFP) where the point of sudden motion changes is also proposed. The proposed hand gesture recognition system extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. And then, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code finally, we obtain the current timing pattern of beat and tempo of the playing music. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% fer the CFP tracking only.

• 산업기술연구
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• 제29권A호
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• pp.47-54
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• 2009

In this paper, we proposed hand detection and hand gesture recognition from USB camera video. Firstly, we extract hand region extraction using skin color information from a difference images. Background image is initially stored and extracted from the input images in order to reduce problems from complex backgrounds. After that, 16-directional chain code sequence is computed from the tracking of hand motion. These chain code sequences are compared with pre-trained models using DP matching. Our hand gesture recognition system can be used to control PowerPoint slides or applied to multimedia education systems. We got 92% hand region extraction accuracy and 82.5% gesture recognition accuracy, respectively.