• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.486-489
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• 2016

가상현실 기반 훈련시스템은 3D 모델링 기법으로 개발되어 실시간 렌더링(Realtime Rendering)되는 훈련용 콘텐츠와 운동감 제공을 위한 모션플랫폼, 촉감 제공을 위한 햅틱장치 등 다양한 하드웨어를 이용하여 인간 감각에 대한 모의 체험환경을 제공함으로써 높은 훈련 몰입감을 제공한다. 훈련시스템의 구성요소 중 하드웨어들은 설계 성능을 바탕으로 정량적 시험평가로서 검증(Verification)과 확인(Validation)이 가능하나, 훈련환경에 대한 사실적 가시화가 요구되는 훈련용 콘텐츠는 시현을 위한 실시간 렌더링 성능 등의 정량적 시험평가 만으로 검증과 확인에 어려움이 많다. 본 연구에서는 일반 소프트웨어와 콘텐츠 소프트웨어 테스팅 요소 차이와 상용게임 콘텐츠와 훈련용 콘텐츠의 차이점을 분석하고, 훈련용 콘텐츠의 정량적 시험평가를 위한 명세서의 작성과 활용을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.823-825
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• 2014

감시 카메라 환경에서 자동으로 그룹 행동을 인식하는 기술이 최근 많은 관심을 받고 있다. 본 논문에서 제안하는 그룹 해동 인식 시스템은 다른 추가 정보 없이 비디오 프레임만을 인풋으로 받아들여, 자동으로 보행자 탐지, 추적, 행동 인식까지 모두 포괄하는 시스템이다. 시공간 모션 패턴을 만들고 연결 요소들로 모델링 한 뒤 Hidden Markov Model (HMM)을 이용해 그룹 행동을 인식한다. 실험 결과, 기본 논문과 비교하였을 때, 비슷한 인식률을 보이면서 수행 시간을 약 25 배 정도로 획기적으로 단축하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1332-1335
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• 2013

본 논문에서는 키넥트 센서 데이터에 은닉 조건부 랜덤 필드 모델을 적용하여 인간의 일상 행위를 인식하는 시스템을 제안한다. 많은 고수준의 일상 행위들은 다수의 부속 행위들이 순차적 혹은 반복적으로 수행되어 나타나는 하나의 계층구조로 볼 수 있다. 따라서 제안하는 시스템에서는 이러한 고수준의 일상 행위들을 순차성과 계층성을 잘 표현할 수 있는 확률 그래프 모델의 하나인 은닉 조건부 랜덤 필드 모델로 모델링함으로써, 행위 인식률을 높이려고 시도하였다. 또한 제안하는 시스템에서는 효과적인 행위 모델의 학습과 적용을 위해, 모션 특징, 구조 특징, 손 위치 특징과 같은 다양한 종류의 특징들을 키넥트 센서 데이터로부터 추출하여 이들을 이용하였다. 그리고 12 가지 일상 행위들에 관한 코넬 대학의 CAD-60 데이터 집합을 이용한 다양한 실험을 통해, 제안하는 시스템의 우수한 인식 성능을 확인할 수 있었다.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.712-721
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• 2010

This paper proposes a new fire-flame detection method using probabilistic membership function of visual features and Fuzzy Finite Automata (FFA). First, moving regions are detected by analyzing the background subtraction and candidate flame regions then identified by applying flame color models. Since flame regions generally have continuous and an irregular pattern continuously, membership functions of variance of intensity, wavelet energy and motion orientation are generated and applied to FFA. Since FFA combines the capabilities of automata with fuzzy logic, it not only provides a systemic approach to handle uncertainty in computational systems, but also can handle continuous spaces. The proposed algorithm is successfully applied to various fire videos and shows a better detection performance when compared with other methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.288-295
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• 2011

The paper proposes a localization method of an underwater robot using Monte Carlo Localization(MCL) approach. Localization is one of the fundamental basics for autonomous navigation of an underwater robot. The proposed method resolves the problem of accumulation of position error which is fatal to dead reckoning method. It deals with uncertainty of the robot motion and uncertainty of sensor data in probabilistic approach. Especially, it can model the nonlinear motion transition and non Gaussian probabilistic sensor characteristics. In the paper, motion model is described using Euler angles to utilize the MCL algorithm for position estimation of an underwater robot. Motion model and sensor model are implemented and the performance of the proposed method is verified through simulation.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1543-1550
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• 2004

Modeling from images is a cost-effective means of obtaining 3D geometric models. These models can be effectively constructed from classical Structure from Motion algorithm. However, it's too difficult to reconstruct whole scenes using SFM method since general sites contain a very complex shapes and brilliant colours. To overcome this difficulty, the current paper proposes a new reconstruction method based on a moving Planar mirror. We devise the mirror posture instead of scene itself as a cue for reconstructing the geometry That implies that the geometric cues are inserted into the scene by compulsion. With this method, we can obtain the geometric details regardless of the scene complexity. For this purpose, we first capture image sequences through the moving mirror containing the interested scene, and then calibrate the camera through the mirror's posture. Since the calibration results are still inaccurate due to the detection error, the camera pose is revised using frame-correspondence of the comer points that are easily obtained using the initial camera posture. Finally, 3D information is computed from a set of calibrated image sequences. We validate our approach with a set of experiments on some complex objects.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.471-476
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• 2016

In this paper, recognition system for continuous human action is explained by using motion history image and histogram of oriented gradient with spotter model based on depth information, and the spotter model which performs action spotting is proposed to improve recognition performance in the recognition system. The steps of this system are composed of pre-processing, human action and spotter modeling and continuous human action recognition. In pre-processing process, Depth-MHI-HOG is used to extract space-time template-based features after image segmentation, and human action and spotter modeling generates sequence by using the extracted feature. Human action models which are appropriate for each of defined action and a proposed spotter model are created by using these generated sequences and the hidden markov model. Continuous human action recognition performs action spotting to segment meaningful action and meaningless action by the spotter model in continuous action sequence, and continuously recognizes human action comparing probability values of model for meaningful action sequence. Experimental results demonstrate that the proposed model efficiently improves recognition performance in continuous action recognition system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1828-1836
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• 2015

Object tracking algorithms can reduce computational cost by avoiding computation over the whole image through the selection of region of interests based on object detection. So, accurate object detection is an important task for object tracking. The background subtraction algorithm has been widely used in moving object detection using a stationary camera. However, it has the problem of object detection error due to incorrect background modeling, whereas the method of background modeling has been improved by many researches. This paper proposes a new moving object detection algorithm to overcome the drawback of the conventional background subtraction algorithm by combining the background subtraction algorithm with the motion history image algorithm that is usually used in gesture detection. Although the proposed algorithm demands more processing time because of time taken for combining two algorithms, it meet the real-time processing requirement. Moreover, experimental results show that it has higher accuracy compared with the previous two algorithms.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.824-834
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• 2011

In this study, the method to generate facial animation of SVG format based online character AUtomatically was proposed. The re-targeted character can reproduce the modeling facial movements itself without losing the unique formative properties by modeling abundant facial movements of the character in the existing animation for movie theater through the mathematical calculation and 'retargeting' this to other character of SVG format. The method of retargeting facial movements like this can be applied to the various characters by only one facial data so although non-specialists, even not an animator can create facial animation of character easily and rapidly. The suggested facial movement in this study is nonlinear and an exaggerated motion so it is friendly to the viewers and it is lively. Besides, it is based on the SVG format, so the smooth facial movements can be implemented by less capacity than existing GIF animation.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.31-38
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• 2024

This study proposes a novel approach to enhancing the social inclusion and participation of individuals with developmental disabilities. Utilizing cutting-edge virtual reality (VR) technology, we designed and developed a metaverse simulator that enables individuals with developmental disabilities to safely and conveniently experience indoor handicapped handball sports. This simulator provides an environment where individuals with disabilities can experience and practice handball matches. For the modeling and animation of handball players, we employed advanced modeling and motion capture technologies to accurately replicate the movements required in handball matches. Additionally, we ported various training programs, including basic drills, penalty throws, and target games, onto XR (Extended Reality) devices. Through this research, we have explored the development of immersive assistive tools that enable individuals with developmental disabilities to more easily participate in activities that may be challenging in real-life scenarios. This is anticipated to broaden the scope of social participation for individuals with developmental disabilities and enhance their overall quality of life.