• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.401-403
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• 1999

본 논문에서 우리는 제약이 없는 배경화면에서 얼굴의 움직임을 이용한 응시점 추적을 위해 얼굴의 특징점(눈, 코, 그리고 입)들을 찾고 head orientation을 구하는 효？거이고 빠른 방법을 제안한다. 얼굴을 찾는 방법이 많이 연구 되어 오고 있으나 많은 부분이 효과적이지 못하거나 제한적인 사항을 필요로 한다. 본 논문에서 제안한 방법은 이진화된 이미지에 기초하고 완전 그래프 매칭을 이용한 유사성을 구하는 방법이다. 즉, 임의의 임계치 값에 의해 이진화된 이미지를 레이블링 한 후 각 쌍의 블록에 대한 유사성을 구한다. 이때 두 눈과 가장 유사성을 갖는 두 블록을 눈으로 선택한다. 눈을 찾은 후 입과 코를 찾아간다. 360$\times$240 이미지의 평균 처리 속도는 0.2초 이내이고 다음 탐색영역을 예상하여 탐색 영역을 줄일 경우 평균 처리속도는 0.15초 이내였다. 그리고 본 논문에서는 얼굴의 움직임을 구하기 위해 각 특징점들이 이루는 각을 기준으로 한 템플릿 매칭을 이용했다. 실험은 다양한 조명환경과 여러 사용자를 대상으로 이루어졌고 속도와 정확성면에서 좋은 결과를 보였다. 도한, 명안정보만을 사용하므로 흑백가메라에서도 사용가능하여 경제적 효과도 기대할 수 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.164-172
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• 2000

To follow a moving target keeping a certain distance it is essential for a mobile robot to detect the target first and to measure its pose and velocity. This paper proposes a new solution for this problem using the accumulated ultrasonic image which is constructed by accumulating the returned ultrasonic signal along the time axis for a certain number of measurement periods. A moving target is separated by selecting the trajectory whose inclination is different from others in the imag since the inclination of a trajectory represents the relative speed of the target against the mobile robot. The proposed algorithm was implemented on a mobile robot and has shown that the robot follows a moving target successfully.

• Journal of the HCI Society of Korea
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• v.11 no.1
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• pp.39-45
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• 2016

Effective and efficient estimation of camera poses is a core method in implementing augmented reality systems or applications. The most common one is using markers, e.g., ARToolkit. However, use of markers suffers from a notorious problem that is vulnerable to occlusion. To overcome this, this paper proposes a top-down method that iteratively estimates the current camera pose by using particle swarm optimization. Through experiments, it was confirmed that the proposed method enables to implement augmented reality on severely-occluded markers.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2414-2416
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• 2003

In this raper, we present a Wavelet Neural Network(WNN) approach to the solution of the tracking problem for mobile robots that possess complexity, nonlinearity and uncertainty. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problems caused by local minima of optimization and various uncertainties. This network structure is helpful to determine the number of the hidden nodes and the initial value of weights with compact structure. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by the gradient-descent method. Through computer simulations, we demonstrate the effectiveness and feasibility of the proposed control method.

• ETRI Journal
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• v.43 no.4
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• pp.617-629
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• 2021

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.11
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• pp.810-816
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• 2014

The alternative binary offset carrier (AltBOC) signal shares frequency spectrum with the phase shift keying (PSK) signal, enabling us to manage the satellite communication spectrum more efficiently. However, the side-peaks in the AltBOC autocorrelation pose an ambiguity in the AltBOC signal tracking, and consequently, makes the frequency sharing via the AltBOC difficult. Therefore, in this paper, we propose a cancellation scheme of the AltBOC correlation side-peaks. From the numerical results, it is confirmed that the proposed scheme removes the ambiguity in signal tracking caused by the side-peaks completely, and thus, has a much lower tracking error standard deviation (TESD) (i.e., a much better tracking performance) than the conventional scheme.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.502-506
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• 2010

An augmented book is an application that augments such multimedia elements as virtual 3D objects generated by computer graphics, movie clips, or sound clips to a real book using AR technologies. It is intended to bring additional education and entertainment effects to users. For augmented books, this paper proposes an adaptive keyframe-based page tracking method to estimate the camera's 6 DOF pose in real-time after recognizing a page and performing wide-baseline keypoint matching. For a page tracking, proposed method in this paper chooses a proper keyframe and performs a tracking in two step of coarse-to-fine stage. As a result, the proposed method in this paper guarantees a robust tracking to view-point and illumination variations and real-time.

• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.963-976
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• 2012

'Gesture' except for voice is the most intuitive means of communication. Thus, many researches on how to control computer using gesture are in progress. User detection and tracking in these studies is one of the most important processes. Conventional 2D object detection and tracking methods are sensitive to changes in the environment or lights, and a mix of 2D and 3D information methods has the disadvantage of a lot of computational complexity. In addition, using conventional 3D information methods can not segment similar depth object. In this paper, we propose object detection and tracking method using Depth Projection Map that is the cumulative value of the depth and motion information. Simulation results show that our method is robust to changes in lighting or environment, and has faster operation speed, and can work well for detection and tracking of similar depth objects.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.272-277
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• 2010

AR(Augmented Reality) needs medium between real and virtual, world, and recognition techniques are necessary to track an object continuously. Optical tracking using marker is mainly used, but it takes time and is inconvenient to attach marker onto the target objects. Therefore, many researchers try to develop markerless tracking techniques nowaday. In this paper, we extract features and 3D position from 3D objects and suggest realtime tracking based on these features and positions, which do not use just coplanar features and 2D position. We extract features using SURF, get rotation matrix and translation vector of 3D object using POSIT with these features and track the object in real time. If the extracted features are nor enough and it fail to track the object, then new features are extracted and re-matched to recover the tracking. Also, we get rotation in matrix and translation vector of 3D object using POSIT and track the object in real time.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.