• Title, Summary, Keyword: 손 동작 인식

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Real-Time Hand Pose Tracking and Finger Action Recognition Based on 3D Hand Modeling (3차원 손 모델링 기반의 실시간 손 포즈 추적 및 손가락 동작 인식)

  • Suk, Heung-Il;Lee, Ji-Hong;Lee, Seong-Whan
    • 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.

Presentation control of a computer using hand motion identification rules (손동작 식별 규칙을 이용한 컴퓨터의 프레젠테이션 제어)

  • Lee, Kyu-Won
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.9
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    • pp.1172-1178
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    • 2018
  • A system that control computer presentations by using the hand motion recognition and identification is proposed. The system recognizes and identifies various types of motion in hand motion, controlls the presentation without additional control devices. To recognize hand movements, it performs a face and hand region detection. Facial area is detected using Haar classifier and hand region is extracted according to skin color information on HSV color model. The face area is used to determine the beginning and end of hand gestures, the size and direction of motion. It recognizes various hand gestures and uses them to control computer presentations according to the hand motion identification rules that are proposed and set horizontal and vertical axes from the face area. It is confirmed that 97.2% recognition rate is obtained in about 1200 hand motion recognition experiments and the proposed algorithm is valid in presentation control.

Incremental User Adaptation in Korean Sign Language Recognition Using Motion Similarity and Prediction from Adaptation History (동작 유사도와 적응 추이를 이용한 한국 수화 인식에서의 사용자에 대한 적응)

  • Jung, Seong-Hoon;Park, Kwang-Hyun;Bien, Zeung-Nam
    • 한국HCI학회:학술대회논문집
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    • pp.386-392
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    • 2007
  • 최근 들어 손 제스처를 인간-기계 인터페이스에 활용하는 연구가 많아지고 있다. 그 중에서 수화 인식은 청각장애인과 일반인 사이의 원활한 의사 소통을 하게 해 주는 인터페이스로서 중요성이 날로 더해가고 있다. 하지만 기존의 수화 인식 연구는 사용자 개개인의 수화 동작의 차이를 고려하지 않고 다수 사용자를 위한 모델을 사용하기 때문에 사용자에 따라 인식률이 낮아지게 된다. 이러한 점을 보완하기 위해 본 논문에서는 개개인의 수화 동작 특성을 반영하여 시스템이 사용자에게 적응해 가는 과정을 다루고자 한다. 특히 점진적인 사용자 적응에 있어서 가장 문제가 되는 것은 어떻게 비관측된 상태(unobserved state)의 파라미터를 수정할 것인가 하는 것이다. 이를 위해서 본 논문에서는 동작 유사도와 적응 추이에 의한 추정을 통해 비관측된 상태의 모델 파라미터를 수정한다. 실제 청각 장애인들로부터 획득한 데이터베이스를 사용하여 제안한 방법이 기존 방법에 비해 더욱 빠르게 사용자의 특성을 시스템에 반영하고 인식률을 향상시킨다는 것을 실험을 통해 보인다.

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Sensor-based Recognition of Human's Hand Motion for Control of a Robotic Hand (로봇 핸드 제어를 위한 센서 기반 손 동작 인식)

  • Hwang, Myun Joong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.9
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    • pp.5440-5445
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    • 2014
  • Many studies have examined robot control using human bio signals but complicated signal processing and expensive hardware are necessary. This study proposes a method to recognize a human's hand motion using a low-cost EMG sensor and Flex sensor. The method to classify movement of the hand and finger is determined from the change in output voltage measured through MCU. The analog reference voltage is determined to be 3.3V to increase the resolution of movement identification through experiment. The robotic hand is designed to realize the identified movement. The hand has four fingers and a wrist that are controlled using pneumatic cylinders and a DC servo motor, respectively. The results show that the proposed simple method can realize human hand motion in a remote environment using the fabricated robotic hand.

HMM-based Motion Recognition with 3-D Acceleration Signal (3차원 가속도 데이터를 이용한 HMM 기반의 동작인식)

  • Kim, Sang-Ki;Park, Gun-Hyuk;Jeon, Seok-Hee;Yim, Sung-Hoon;Han, Gab-Jong;Choi, Seung-Moon;Choi, Seung-Jin
    • Journal of KIISE:Computing Practices and Letters
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    • v.15 no.3
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    • pp.216-220
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    • 2009
  • In this paper we propose a motion recognition method for handheld controller 3-D acceleration signals, generated by 3 axis accelerometer in the controller, are transmitted to the computer by Bluetooth communication. We extract motion segments from continuous acceleration signals and apply to each motion model, which is trained in training phase. Hidden Markov Model was used to model each motion. We applied proposed method to three motion sets, the recognition result was good enough to practical use.

Gesture Recognition based on Motion Inertial Sensors for Interactive Game Contents (체험형 게임콘텐츠를 위한 움직임 관성센서 기반의 제스처 인식)

  • Jung, Young-Kee;Cha, Byung-Rae
    • The Journal of Advanced Navigation Technology
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    • v.13 no.2
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    • pp.262-271
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    • 2009
  • The purpose of this study was to propose the method to recognize gestures based on inertia sensor which recognizes the movements of the user using inertia sensor and lets the user enjoy the game by comparing the recognized movements with the pre-defined movements for the game contents production. Additionally, it was tried to provide users with various data entry methods by letting them wear small controllers using three-axis accelerator sensor. Users can proceed the game by moving according to the action list printed on the screen. They can proceed the experiential games according to the accuracy and timing of their movements. If they use multiple small wireless controllers together wearing them on the major parts of hands and feet and utilize the proposed methods, they will be more interested in the game and be absorbed in it.

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Hand Gesture Recognition Algorithm Robust to Complex Image (복잡한 영상에 강인한 손동작 인식 방법)

  • Park, Sang-Yun;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.13 no.7
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    • pp.1000-1015
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    • 2010
  • In this paper, we propose a novel algorithm for hand gesture recognition. The hand detection method is based on human skin color, and we use the boundary energy information to locate the hand region accurately, then the moment method will be employed to locate the hand palm center. Hand gesture recognition can be separated into 2 step: firstly, the hand posture recognition: we employ the parallel NNs to deal with problem of hand posture recognition, pattern of a hand posture can be extracted by utilize the fitting ellipses method, which separates the detected hand region by 12 ellipses and calculates the white pixels rate in ellipse line. the pattern will be input to the NNs with 12 input nodes, the NNs contains 4 output nodes, each output node out a value within 0~1, the posture is then represented by composed of the 4 output codes. Secondly, the hand gesture tracking and recognition: we employed the Kalman filter to predict the position information of gesture to create the position sequence, distance relationship between positions will be used to confirm the gesture. The simulation have been performed on Windows XP to evaluate the efficiency of the algorithm, for recognizing the hand posture, we used 300 training images to train the recognizing machine and used 200 images to test the machine, the correct number is up to 194. And for testing the hand tracking recognition part, we make 1200 times gesture (each gesture 400 times), the total correct number is 1002 times. These results shows that the proposed gesture recognition algorithm can achieve an endurable job for detecting the hand and its' gesture.

A Study on Comparative Experiment of Hand-based Interface in Immersive Virtua Reality (몰입형 가상현실에서 손 기반 인터페이스의 비교 실험에 관한 연구)

  • Kim, Jinmo
    • Journal of The Korea Computer Graphics Society
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    • v.25 no.2
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    • pp.1-9
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    • 2019
  • This study compares hand-based interfaces to improve a user's virtual reality (VR) presence by enhancing user immersion in VR interactions. To provide an immersive experience, in which users can more directly control the virtual environment and objects within that environment using their hands and, to simultaneously minimize the device burden on users using immersive VR systems, we designed two experimental interfaces (hand motion recognition sensor- and controller-based interactions). Hand motion recognition sensor-based interaction reflects accurate hand movements, direct gestures, and motion representations in the virtual environment, and it does not require using a device in addition to the VR head mounted display (HMD). Controller-based interaction designs a generalized interface that maps the gesture to the controller's key for easy access to the controller provided with the VR HMD. The comparative experiments in this study confirm the convenience and intuitiveness of VR interactions using the user's hand.

Implementing user interface through everyday gesture (일상적 행동양식을 통한 인터페이스의 구현)

  • Ahn, Jong-Yoon;Lee, Kyung-Won
    • 한국HCI학회:학술대회논문집
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    • pp.409-415
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    • 2006
  • 컴퓨터와 인간사이의 원활한 의사소통 및 인터랙션을 위해 기존의 키보드, 마우스를 대체할 수 있는 다양한 입력장치들이 개발되고 있다. 하지만 정보를 탐색, 접근하는 데에 있어서 기존의 장치들은 클릭과 같은 제한적인 동작만을 입력 값으로 받아들이므로 이러한 방식에 익숙하지 않은 사용자의 입장에서는 부자연스러움을 느끼는 요인이 된다. 사용자의 제스처를 인식할 수 있는 인터페이스를 통해 일상에서 사물을 사용할 때의 행동양식을 그대로 가져올 수 있다면, 디지털 콘텐츠에 접근하는데 있어 보다 직관적이고 편리하게 컴퓨터와 의사소통 될 수 있다. 제스처는 동작의 자율성이 높고 때로 그 의미를 파악하기 모호하기 때문에 동작들을 정확히 인식하여 구분할 필요가 있다. 본 논문에서는 이를 바탕으로 효과적인 제스처 인터페이스의 구현을 위해 필요한 점들을 살펴보고, 기술적 구현을 통해 디지털 콘텐츠와의 인터랙션을 보여주고자 한다. 정보 접근에 있어 가장 익숙하고 전통적이라 할 수 있는 책의 메타포를 통해 페이지를 넘기는 행동양식을 인식할 수 있는 인터페이스를 개발하고 이를 입력장치로 사용한다. 사용자의 동작을 인식, 파악하여 책을 앞뒤로 넘기거나 탐색하며 원하는 정보에 접근할 수 있도록 유도하고 손 동작을 통한 인터페이스를 수단으로 컴퓨터와의 유연한 의사소통이 가능하도록 구현한다.

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A Study on Hand Detection using Deep Learning (딥러닝을 이용한 손검출에 관한 연구)

  • Pak, Myeong-Suk;Kim, Sang-Hoon
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.471-473
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    • 2018
  • 딥러닝은 이미지 분류 및 객체 검출과 같은 여러 컴퓨터 비전 관련 작업에 성공적으로 사용되었다. 손 검출은 인간 컴퓨터 상호작용 분야에서 손 분류 및 손 동작 인식을 위한 매우 중요한 부분이며 딥러닝을 사용하여 시도되었다. 본 연구에서는 손 데이터 셋을 이용하여 컨볼루션 신경망을 훈련시킨 다음 학습된 특징을 시각화하고, CNN 아키텍처와 손 데이터 셋의 결과를 각각 살펴보며 손 검출에 대한 이해를 제공한다.