• Smart Media Journal
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• v.1 no.2
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• pp.40-46
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• 2012

In this paper, we describe interactive contents which is used the result of the inputted interface recognizing vision-based body gesture. Because the content uses the imp which is the common culture as the subject in Asia, we can enjoy it with culture familiarity. And also since the player can use their own gesture to fight with the imp in the game, they are naturally absorbed in the game. And the users can choose the multiple endings of the contents in the end of the scenario. In the part of the gesture recognition, KINECT is used to obtain the three-dimensional coordinates of each joint of the limb to capture the static pose of the actions. The vision-based 3D human pose recognition technology is used to method for convey human gesture in HCI(Human-Computer Interaction). 2D pose model based recognition method recognizes simple 2D human pose in particular environment On the other hand, 3D pose model which describes 3D human body skeletal structure can recognize more complex 3D pose than 2D pose model in because it can use joint angle and shape information of body part Because gestures can be presented through sequential static poses, we recognize the gestures which are configured poses by using HMM In this paper, we describe the interactive content which is used as input interface by using gesture recognition result. So, we can control the contents using only user's gestures naturally. And we intended to improve the immersion and the interest by using the imp who is used real-time interaction with user.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.83-95
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• 2014

Hand posture recognition is an important technique to enable a natural and familiar interface in HCI(human computer interaction) field. In this paper, we introduce a hand posture recognition method by using a depth camera. Moreover, the hand posture recognition method is incorporated with MPEG-U based advanced user interaction (AUI) interface system, which can provide a natural interface with a variety of devices. The proposed method initially detects positions and lengths of all fingers opened and then it recognizes hand posture from pose of one or two hands and the number of fingers folded when user takes a gesture representing a pattern of AUI data format specified in the MPEG-U part 2. The AUI interface system represents user's hand posture as compliant MPEG-U schema structure. Experimental results show performance of the hand posture recognition and it is verified that the AUI interface system is compatible with the MPEG-U standard.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.618-632
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• 2010

This paper presents a novel approach to control the augmented reality (AR) objects robustly in a marker-less AR system by fingertip tracking and hand pattern recognition. It is known that one of the promising ways to develop a marker-less AR system is using human's body such as hand or face for replacing traditional fiducial markers. This paper introduces a real-time method to manipulate the overlaid virtual objects dynamically in a marker-less AR system using both hands with a single camera. The left bare hand is considered as a virtual marker in the marker-less AR system and the right hand is used as a hand mouse. To build the marker-less system, we utilize a skin-color model for hand shape detection and curvature-based fingertip detection from an input video image. Using the detected fingertips the camera pose are estimated to overlay virtual objects on the hand coordinate system. In order to manipulate the virtual objects rendered on the marker-less AR system dynamically, a vision-based hand control interface, which exploits the fingertip tracking for the movement of the objects and pattern matching for the hand command initiation, is developed. From the experiments, we can prove that the proposed and developed system can control the objects dynamically in a convenient fashion.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.596-598
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• 2019

손동작 인식을 기반으로 한 Virtual fitting 시스템은 Kinect Sensor 를 사용하여 자연스러운 Fitting 을 구현할 수 있다. Kinect Sensor 를 이용한 Pose estimation, Gesture recognition, Virtual fitting 을 구현함으로써 가상으로 의복을 착용하는 소프트웨어를 소개한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.787-789
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• 2004

본 논문에서는 별도의 센서를 부착하지 않고 영상만을 이용하여 실시간으로 손 형상을 인식하는 알고리즘에 대해 기술한다. 손은 형상이 매우 복잡하기 때문에 2차원 형상의 불변량에 해당하는 에지의 방향성 히스토그램을 이용하여 인식을 행한다. 이 방법은 복잡한 배경에서 색상정보를 이용하여 손 영역이 정확히 추출되면 계산량이 적고 조명변화에 덜 민감하기 때문에 실시간 손 형상 인식에 적합하다. 본 논문에서는 손의 형상제시 방향이 변하는 경우에도 인식을 가능하게 하기 위해 주성분 분석법을 사용하여 인식오차를 줄이는 방법을 기술한다. 이 방법을 사용함으로써 손 형상이 3차원적으로 회전에 의해 변하는 경우도 인식가능하게 되었다. 실험부분에서 제안하는 방법을 이용하여 가정용 가전제품이나 게임을 제어하는 실시간 휴먼 인터페이스 시스템 제작기술로 활용될 수 있음을 보인다.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.780-788
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• 2008

Modeling hand poses and tracking its movement are one of the challenging problems in computer vision. There are two typical approaches for the reconstruction of hand poses in 3D, depending on the number of cameras from which images are captured. One is to capture images from multiple cameras or a stereo camera. The other is to capture images from a single camera. The former approach is relatively limited, because of the environmental constraints for setting up multiple cameras. In this paper we propose a method of reconstructing 3D hand poses from a 2D input image sequence captured from a single camera by means of Belief Propagation in a graphical model and recognizing a finger clicking motion using a hidden Markov model. We define a graphical model with hidden nodes representing joints of a hand, and observable nodes with the features extracted from a 2D input image sequence. To track hand poses in 3D, we use a Belief Propagation algorithm, which provides a robust and unified framework for inference in a graphical model. From the estimated 3D hand pose we extract the information for each finger's motion, which is then fed into a hidden Markov model. To recognize natural finger actions, we consider the movements of all the fingers to recognize a single finger's action. We applied the proposed method to a virtual keypad system and the result showed a high recognition rate of 94.66% with 300 test data.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1143-1148
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• 2006

사용자 친화형 유저 인터페이스 구현을 위해 인간의 손 형상을 실시간으로 인식하는 연구의 중요성이 부각되고 있다. 그러나 인간의 손은 자유도가 크기 때문에 손 형상을 정확히 인식하기란 매우 어렵고 또한 피부색과 유사한 색을 가지는 복잡한 배경에서는 더욱 곤란하다. 본 논문에서는 별도의 센서를 부착하지 않고 카메라를 사용하여 피부색 정보에 의한 손 형상을 분할한 후 손가락 끝 점을 찾는다. 찾은 손가락 끝점을 이용하여 방향을 탐지하는 알고리즘에 대해 기술한다. 이 방법은 템플리트 매칭을 이용하여 손가락 끝 점을 탐색한 후 찾은 손 가락 끝 점과 손목의 중심을 이용하여 전, 후, 좌, 우 방향을 탐지한다. 제안하는 방법을 이용하여 3D가상현실 공간에서의 Navigation에 응용하였으며, 실험결과 전진, 후진 및 좌측, 우측의 방향전환도 매우 좋은 결과를 보였다. 또한 본 논문에서 제안하는 방법은 마우스, 키보드, 조이스틱 등의 조작 없이 전, 후, 좌, 우 방향전환을 사용자가 직관적으로 지시함으로써 보다 자연스러운 인간과 컴퓨터의 상호작용을 제공할 수 있을 것이다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1574-1580
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• 2020

Traditional pose estimation methods include using special devices or images through image processing. The disadvantage of using a device is that the environment in which the device can be used is limited and costly. The use of cameras and image processing has the advantage of reducing environmental constraints and costs, but the performance is lower. CNN(Convolutional Neural Networks) were studied for pose estimation just using only camera without these disadvantage. Various techniques were proposed to increase cognitive performance. In this paper, the effect of the skip connection on the network was experimented by using various skip connections on the joint recognition of the hand. Experiments have confirmed that the presence of additional skip connections other than the basic skip connections has a better effect on performance, but the network with downward skip connections is the best performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.171-180
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• 2013

Generally, when we apply zernike moments value for matching, we can use those moments value obtained from projecting image information under circumscribed circle to zernike basis function. However, the problem is that the power of discrimination can be reduced because hand images include lots of overlapped information due to its special characteristic. On the other hand, when distinguishing hand poses, information in specific area of image information except for overlapped information can increase the power of discrimination. In this paper, in order to solve problems like those, we design R3 ring mask by combining image obtained from R2 ring mask, which can weight information of the power of discrimination and image obtained from R1 ring mask, which eliminate the overlapped information. The moments which are obtained by R3 ring mask decrease operational time by reducing dimension through principle component analysis. In order to confirm the superiority of the suggested method, we conducted some experiments by comparing our method to other method using seven different hand poses.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1499-1502
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• 2003

본 논문에서는 손 자세 인식을 위해 손 영상을 소그룹으로 나누고 최종적으로 소그룹 내에서 개별 모델로 분류하는 다단계 접근 방식을 취한다. 이 방식은 처음부터 모든 특성치들을 다 구하여 기존에 가지고 있는 모델 모두와 비교하는 대신, 먼저 소그룹으로 분류 후에 해당 소그룹 내의 모델만을 대상으로 비교 연산을 수행한다. 따라서 계산 량을 크게 줄일 수 있을 뿐 아니라, 확장이 용이하며, 각 소그룹 별로 특성화된 처리를 할 수 있으므로 효율적인 인식기의 구현이 가능하다.