• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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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.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.47-53
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• 2024

In this paper, we propose applying interactive technology in virtual environments to enhance interaction and immersion by inducing more natural movements in the gesture recognition process through the concept of affordance. A technique is proposed to recognize gestures most similar to actual hand movements by applying a line segment recognition algorithm, incorporating sampling and normalization processes in the gesture recognition process. This line segment recognition was applied to the drawing of magic circles in the <VR Spell> game implemented in this paper. The experimental method verified the recognition rates for four line segment recognition actions. This paper aims to propose a VR game that pursues greater immersion and fun for the user through real-time hand tracking technology using affordance Elements, applied to immersive content in virtual environments such as VR games.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3971-3975
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• 2013

This paper introduces an Augmented Reality system recognizing hand gestures and shows results of the evaluation. The system's user can interact with artificial objects and manipulate their position and motions simply by his hand gestures. Hand gesture recognition is based on Histograms of Oriented Gradients (HOG). Salient features of human hand appearance are detected by HOG blocks. Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification multiclass AdaBoostSVM algorithm is applied. Evaluated recognition rate of the algorithm is 94.0%.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.516-520
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• 2007

This paper presents a intuitive, simple and easy smart home TV remote control system using the hand gesture recognition. Hand candidate regions are detected by cascading policy of the part of human anatomy on the disparity map image, Exact hand region is extracted by the graph-cuts algorithm using the skin color information. Hand postures are represented by shape features which are extracted by a simple shape extraction method. We use the forward spotting accumulative HMMs for a smart home TV remote control system. Experimental results show that the proposed system has a good recognition rate of 97.33 % for TV remote control in real-time.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.21-22
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• 2013

논문에서는 제스처 인식을 하기 위해 필요한 특징 값을 3차원 방향 코드로 구현한 특징 패턴을 검출하는 방법을 제안한다. 검출된 데이터 좌표끼리 직선을 만들고 직선들의 사이각의 합 연산을 이용해서 특징 변곡점을 추출한다. 추출된 변곡점끼리 직선을 생성한 후, 8방향 코드와 깊이 값을 병합시킨 24방향 코드를 맵핑 시켜준다. 맵핑된 방향 코드들을 한 패턴으로 생성한다. 생성된 패턴에서 인식에 불필요한 방향 노이즈를 제거하기 위해 특정 규칙을 적용한 필터링을 적용하여 필터링된 패턴을 추출하게 된다. '배너코드를 이용한 8방향 패턴'과 비교해서 더 효과적인 패턴이 추출됨을 확인하였다.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.11-12
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• 2016

SVM (Support Vector machine) is powerful machine-learning method, and obtains better performance than traditional methods in the applications of muti-dimension nonlinear pattern classification. For the case of SVM model training and low efficiency in large samples, this paper proposes a combination of statistical parameters of the GMM-UBM (Universal Background Model) model. It is very effective to solve the problem of the large sample for the SVM training. The experiment is carried on four special dynamic hand gestures using the EPIC sensors. And the results show that the improved dynamic hand gesture recognition system has a high recognition rate up to 96.75%.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2930-2932
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• 2000

In this paper. a robust gesture recognition system is designed and implemented to explore the communication methods between human and computer. Hand gestures in the proposed approach are used to communicate with a computer for actions of a high degree of freedom. The user does not need to wear any cumbersome devices like cyber-gloves. No assumption is made on whether the user is wearing any ornaments and whether the user is using the left or right hand gestures. Image segmentation based upon the skin-color and a shape analysis based upon the invariant moments are combined. The features are extracted and used for input vectors to a radial basis function networks(RBFN). Our "Puppy" robot is employed as a testbed. Preliminary results on a set of gestures show recognition rates of about 87% on the a real-time implementation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.85-91
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• 1995

This paper presents a system which recognizes the Korean Sign Language(KSL) and translates into normal Korean speech. A sign language is a method of communication for the deaf-mute who uses gestures, especially both hands and fingers. Since the human hands and fingers are not the same in physical dimension, the same form of a gesture produced by two signers with their hands may not produce the same numerical values when obtained through electronic sensors. In this paper, we propose a dynamic gesture recognition method based on feature analysis for efficient classification of hand motions, and on a fuzzy min-max neural network for on-line pattern recognition.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.375-377
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• 2019

본 논문에서는 관람객 얼굴 표정 및 제스쳐 인식을 기반으로 인터렉티브 디지털콘텐츠의 문제점 추론 시스템을 제안한다. 관람객이 콘텐츠를 체험하고 다른 장소로 이동하기 전까지의 행동 패턴을 기준으로 삼아 4가지 문제점으로 분류한다. 문제점 분류을 위해 관람객이 콘텐츠 체험과정에서 나타낼 수 있는 얼굴 표정 3가지 종류와 제스쳐 5가지를 구분하였다. 실험에서는 입력된 비디오로부터 얼굴 및 손을 검출하기 위해 Adaboost algorithm을 사용하였고, mobilenet v1을 retraining하여 탐지모델을 생성 후 얼굴 표정 및 제스쳐를 검출했다. 이 연구를 통해 인터렉티브 디지털콘텐츠가 지니고 있는 문제점을 추론하여 향후 콘텐츠 개선과 제작에 사용자 중심 설계가 가능하도록 하고 양질의 콘텐츠 생산을 촉진 시킬 수 있을 것이다.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.124-131
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• 2014

In this paper, we propose the non-contact gesture recognition algorithm using 4- channel electrometer sensor array. ELF(Extremely Low Frequency) EMI and PLN are minimized because ambient electromagnetic noise around sensors has a significant impact on entire data in indoor environments. In this study, we transform AC-type data into DC-type data by applying a 10Hz LPF as well as a maximum buffer value extracting algorithm considering H/W sampling rate. In addition, we minimize the noise with the Kalman filter and extract 2-dimensional movement information by taking difference value between two cross-diagonal deployed sensors. We implemented the DTW gesture recognition algorithm using extracted data and the time delayed information of peak values. Our experiment results show that average correct classification rate is over 95% on five-gesture scenario.