• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.34-35
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• 2014

본 논문에서는 손가락 제스처 인식을 위해서 깊이 영상 카메라를 이용하여 손 영역을 추적하고 손가락 끝점을 찾는 방법을 제시하고자 한다. 실시간 영역 추적을 위해 적은 연산량으로 손 영역의 중심점을 검출하고 추적이 가능하여야 하며, 다양한 제스처를 효과적으로 인식하기 위해서는 손 모양에서 손가락을 인식하여야 하기 때문에 손가락 끝점을 찾는 방법도 함꼐 제시하고자 한다. 또한 손가락이 정확히 검출되었는지를 확인하기 위해서 손가락의 이동과 손가락의 클릭 제스처를 마우스에 연동하여 검출 결과를 테스트 하였다.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.11-18
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• 2011

In the field of image processing to track the fingertip much research has been done. The most common way to calculate the fingertip first, to extract color information. Then, it uses Blob Coloring algorithms which are expressed in blob functions the skin contour and calculates. The algorithm from contour decides the highest location with the fingertip. But this method when measuring it location from the finger condition which bents is not the actual fingertip and has the problem which detects the location which goes wrong. This paper proposes the color space correction algorithm to tracks the fingertip which bents. The method which proposes when tracking the fingertip from the finger condition which bents solves the problem which measures the location which goes wrong. Aim of this paper in compliance with the propensity of the users forecasts a problem in advance and corrects with improvement at the time of height boil an efficiency. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed method. Accordingly, the satisfaction and the quality of services will be improved the image recognition.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.752-753
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• 2023

본 논문에서는 손가락 관절의 운동범위와 MediaPipe 손 추적기를 이용하여 3 차원 손 추적 방법을 설계하였다. MediaPipe 손 추적기가 추정한 신뢰할 수 있는 2 차원 좌표를 바탕으로 손 랜드마크의 깊이를 추정한 후, 손가락 관절 운동범위와 부합한 결과를 도출하였다. 본 논문에서 제안한 3 차원 손 추적 방법은 전용 하드웨어 없이 동작하며 기존의 3 차원 손 추적기에 비해 보다 직관적인 인간-컴퓨터 인터페이스 확산에 긍정적 영향을 줄 것으로 기대한다.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.847-850
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• 2008

On today, human is going to develop machine by using mutual communication to machine. Including vision - based HCI(Human Computer Interaction), the technique which to recognize finger and to track finger is important in HCI systems, in HCI systems. In order to divide finger, this paper uses more effectively dividing the technique using subtraction which is separation of background and foreground, as well as to divide finger from limited background and cluttered background. In order to divide finger, the finger is recognized to make "Template-Matching" by identified fingertip images. And, identified gestures be compared the tracked gesture after tracking recognized finger. In this paper, after obtaining interest area, not only using subtraction image and template-matching but to perform template-matching in the area. So, emphasis is placed on decreasing perform speed and reaction speed, and we propose technique which is more effectively recognizing gestures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2956-2962
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• 2015

In this paper, we propose a novel algorithm, Concentric-Circle Tracing algorithm, which recognizes finger's shape and counts the number of fingers of hand using low-cost web-camera. We improve algorithm's usability by using low-price web-camera and also enhance user's comfortability by not using a additional marker or sensor. As well as counting the number of fingers, it is possible to extract finger's shape information whether finger is straight or folded, efficiently. The experimental result shows that the finger gesture can be recognized with an average accuracy of 95.48%. It is confirmed that the hand-gesture is an useful method for HCI input and remote control command.

• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.42-47
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• 2014

A finger-gesture game controller using the single camera is implemented in this paper, which is based on the recognition of the number of fingers and the index finger moving direction. Proposed method uses the CAMShift algorithm to trace the end-point of index finger effectively. The number of finger is recognized by using a double circle tracing method. Then, HSI color mode transformation is performed for the CAMShift algorithm, and YCbCr color model is used in the double circle tracing method. Also, all processing tasks are implemented by using the Intel OpenCV library and C++ language. In order to evaluate the performance of the proposed method, we developed a shooting game simulator and validated the proposed method. The proposed method showed the average recognition ratio of more than 90% for each of the game command-mode.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.232-233
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• 2011

본 논문에서는 사용자의 손바닥 위에 증강되는 가상 객체와의 자연스러운 상호 작용을 제공하는 혁신적인 손 기반 인터페이스를 제안한다. 본 인터페이스에서는 한 손바닥 위에 다른 쪽 손의 손가락으로 미리 정의된 패턴을 그린 후 그 패턴을 인식함으로써 가상 객체들이 용이하게 선택되도록 한다. 이렇게 선택된 가상 객체들은 계산된 손바닥 포즈를 이용해서 손바닥 위에 증강된다. 손가락으로 그려진 패턴을 효과적으로 검출하기 위해 손가락을 감쌀 수 있는 마커를 사용하여 손가락의 궤적을 추적한다. 넓은 범위의 손바닥 포즈를 계산하고 손가락의 움직임을 인식함으로써 사용자는 다양한 시각에서 손바닥 위의 증강된 가상 객체와 자연스럽게 상호 작용할 수 있다.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.545-554
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• 2014

In this paper, we propose online Chinese character learning machine with a depth camera, where a system presents a Chinese character on a screen and a user is supposed to draw the presented Chinese character by his or her hand gesture. We develop the hand tracking method and suggest the adjusted chain code to represent constituent strokes of a Chinese character. For hand tracking, a fingertip is detected and verified. The adjusted chain code is designed to contain the information on order and relative length of each constituent stroke as well as the information on the directional variation of sample points. Such information is very efficient for a real-time match process and checking incorrectly drawn parts of a stroke.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.636-642
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• 2017

In this paper, We proposed a novel hand-gesture recognition algorithm using concentric-circle expanding and tracing. The proposed algorithm determines region of interest of hand image through preprocessing the original image acquired by web-camera and extracts the feature of hand gesture such as the number of stretched fingers, finger tips and finger bases, angle between the fingers which can be used as intuitive method for of human computer interaction. The proposed algorithm also reduces computational complexity compared with raster scan method through referencing only pixels of concentric-circles. The experimental result shows that the 9 hand gestures can be recognized with an average accuracy of 90.7% and an average algorithm execution time is 78ms. The algorithm is confirmed as a feasible way to a useful input method for virtual reality, augmented reality, mixed reality and perceptual interfaces of human computer interaction.