• 대한전기학회논문지:시스템및제어부문D
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• 제53권1호
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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.

• 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.

• 한국정보통신학회논문지
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• 제25권12호
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• pp.1770-1776
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• 2021

손 표현 인식을 위해서는 손의 정적인 형태를 기반으로 하는 손 자세 인식과 손의 움직임을 기반으로 하는 손 동작 인식이 함께 사용된다. 본 논문에서는 가상의 칠판 위에서 움직이는 손의 궤적을 기반으로 기호를 인식하는 손 표현인식 방법을 제안하였다. 손으로 가상의 칠판에 그린 기호를 인식하기 위해서는 손의 움직임으로부터 기호를 인식하는 방법은 물론, 데이터 입력의 시작과 끝을 찾아내기 위한 손 자세 인식 역시 필요하다. 본 논문에서는 손 자세 인식을 위해 미디어파이프를, 시계열 데이터에서 손 동작을 인식하기 위해 순환 신경망의 한 종류인 LSTM(Long Short Term Memory)을 사용하였다. 제안하는 방법의 유효성을 보이기 위해 가상 칠판에 쓰는 숫자 인식에 제안하는 방법을 적용하였을 때 약 94%의 인식률을 얻을 수 있었다.

• 한국정보통신학회논문지
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• 제15권7호
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• pp.1564-1572
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• 2011

본 논문에서는 증강현실을 위한 히스토그램 기반의 손 인식 기법을 제안한다. 손동작 인식은 사용자와 컴퓨터 사이의 친숙한 상호작용을 가능하게 한다. 하지만, 비젼 기반의 손동작 인식은 복잡한 손의 형태로 인한 관찰 방향 변화에 따른 입력 영상의 다양함으로 인식에 어려움이 따른다. 따라서 본 논문에서는 손의 형태적인 특징을 이용한 새로운 모델을 제안한다. 제안하는 기법에서 손 인식은 카메라로부터 획득한 영상에서 손 영역을 분리하는 부분과 인식하는 부분으로 구성된다. 카메라로부터 획득한 영상에서 배정을 제거하고 피부색 정보를 이용하여 손 영역을 분리한다. 다음으로 히스토그램을 이용하여 손의 특징점을 구하여 손의 형태를 계산한다. 마지막으로 판별된 손인식 정보를 이용하여 3차원 객체를 제어하는 증강현실 시스템을 구현하였다. 실험을 통해 제안한 기법의 구현 속도가 빠르고 인식률도 91.7%로 비교적 높음을 확인하였다.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.

• IEIE Transactions on Smart Processing and Computing
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• 제5권4호
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• pp.267-273
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• 2016

Hand posture recognition is an important technique to enable a natural and familiar interface in the human-computer interaction (HCI) field. This paper introduces a hand posture recognition method using a depth camera. Moreover, the hand posture recognition method is incorporated with the Moving Picture Experts Group Rich Media User Interface (MPEG-U) Advanced User Interaction (AUI) Interface (MPEG-U part 2), which can provide a natural interface on a variety of devices. The proposed method initially detects positions and lengths of all fingers opened, and then recognizes the hand posture from the pose of one or two hands, as well as the number of fingers folded when a user presents a gesture representing a pattern in the AUI data format specified in MPEG-U part 2. The AUI interface represents a user's hand posture in the compliant MPEG-U schema structure. Experimental results demonstrate the performance of the hand posture recognition system and verified that the AUI interface is compatible with the MPEG-U standard.

• 한국정보통신학회논문지
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• 제25권10호
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• pp.1323-1329
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• 2021

손 표현 인식을 위해서는 손의 정적인 형태를 기반으로 하는 손 자세 인식과 손의 동적인 움직임을 기반으로 하는 손 동작 인식이 함께 사용된다. 이 논문에서는 손 표현 인식을 위해 손가락의 위치와 형태를 기반으로 하는 계층적 손 자세 모델을 제안한다. 손 자세 인식을 위해서는 오픈소스인 미디어파이프를 기반으로 하고, 손가락 상태를 나타내는 모델과 이를 통해 손 자세를 나타내는 모델을 계층적으로 구성하였다. 손가락 모델 역시 손가락 하나의 굽힘과 손가락 두 개의 닿음을 사용하여 계층적으로 구성하였다. 제안하는 모델은 손을 통해 정보를 전달하는 다양한 응용에 사용할 수 있으며, 수화에서의 숫자 인식에 적용하여 그 유용성을 검증하였다. 제안하는 모델은 수화 인식 이외에 컴퓨터의 사용자 인터페이스에서 다양한 응용이 가능할 것으로 기대한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.219-221
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• 2007

Recent changes to ubiquitous computing requires more natural human-computer(HCI) interfaces that provide high information accessibility. Hand-gesture, i.e., gestures performed by one 'or two hands, is emerging as a viable technology to complement or replace conventional HCI technology. This paper deals with hand-posture recognition. Hand-posture database construction is important in hand-posture recognition. Human hand is composed of 27 bones and the movement of each joint is modeled by 23 degrees of freedom. Even for the same hand-posture,. grabbed images may differ depending on user's characteristic and relative position between the hand and cameras. To solve the difficulty in defining hand-postures and construct database effective in size, we present a method using a 3D hand model. Hand joint angles for each hand-posture and corresponding silhouette images from many viewpoints by projecting the model into image planes are used to construct the ?database. The proposed method does not require additional equations to define movement constraints of each joint. Also using the method, it is easy to get images of one hand-posture from many vi.ewpoints and distances. Hence it is possible to construct database more precisely and concretely. The validity of the method is evaluated by applying it to the hand-posture recognition system.

• 제어로봇시스템학회논문지
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• 제17권4호
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• pp.368-374
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• 2011

This paper proposes a robot control human interface using Markov model (HMM) based hand signal recognizer. The command receiving humanoid robot sends webcam images to a client computer. The client computer then extracts the intended commanding hum n's hand motion descriptors. Upon the feature acquisition, the hand signal recognizer carries out the recognition procedure. The recognition result is then sent back to the robot for responsive actions. The system performance is evaluated by measuring the recognition of '48 hand signal set' which is created randomly using fundamental hand motion set. For isolated motion recognition, '48 hand signal set' shows 97.07% recognition rate while the 'baseline hand signal set' shows 92.4%. This result validates the proposed hand signal recognizer is indeed highly discernable. For the '48 hand signal set' connected motions, it shows 97.37% recognition rate. The relevant experiments demonstrate that the proposed system is promising for real world human-robot interface application.