• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.11
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• pp.996-1003
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• 2016

Trilateration technique using time of arrival(TOA) is generally used for single target position estimation in radar system. However, trilateration technique has limitation in case of multiple targets, since it is difficult to distinguish the measurements corresponding to the respective targets. In this study, to eliminate ambiguity of relation between measurements and targets, micromotion of each target is measured by micro Doppler which is actively studied in radar industry nowadays and these information are used to distinguish measurements used at trilateration technique. Resultingly, the trilateration technique is applied successfully for each target. The targets are considered as multiple submissiles separated from the missile. Simulation results shows the performance of the proposed algorithm.

• Journal of IKEEE
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• v.16 no.4
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• pp.382-388
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• 2012

An H.264/AVC fast motion estimation methodology is presented in this paper. Advanced SIMD based NEON which is one of the parallel processing methods is supported under the ARM Cortex-A9 dual-core platform. NEON is applied to a full search technique with one of the various motion estimation methods and SAD operation count of each macroblock is reduced to 1/4. Pixel values of the corresponding macroblock are assigned to eight 16-bit NEON registers and Intrinsic function in NEON architecture carried out 128 bits arithmetic operations at the same time. In this way, the exact motion vector with the minimum SAD value among the calculated SAD values can be designated. Experimental results show that performance gets improved 30% above average in accordance with the size of image and macroblock.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.527-535
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• 2016

Statistical optimization algorithms have been variously developed to estimate the 3D shape and motion. However, statistical approaches are limited to analyze the sensitive effects of SfM(Shape from Motion) according to the camera's geometrical position or viewing angles and so on. This paper propose the quantitative estimation method about the uncertainties of an observation matrix by using camera imaging configuration factors predict the reconstruction ambiguities in SfM. This is a very efficient method to predict the final reconstruction performance of SfM algorithm. Moreover, the important point is that our method show how to derive the active guidelines in order to set the camera imaging configurations which can be expected to lead the reasonable reconstruction results. The experimental results verify the quantitative estimates of an observation matrix by using camera imaging configurations and confirm the effectiveness of our algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1541-1550
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• 2004

In this paper, we propose an advanced hardware architecture for fast multi-resolution motion estimation of the video coding standard MPEG-1,2 and MPEG-4 AVC. We describe the algorithm and derive hardware architecture emphasizing the importance of area for low cost and fast operation by using the shared memory, the special ram architecture, the motion vector for 4 pixel x 4 pixel, the spiral search and so on. The proposed architecture has been verified by ARM-interfaced emulation board using Excalibur Altera FPGA and also by ASIC synthesis using Samsung 0.18 m CMOS cell library. The ASIC synthesis result shows that the proposed hardware can operate at 140 MHz, processing more than 1,100 QCIF video frames or 70 4CIF video frames per second. The hardware is going to be used as a core module when implementing a complete MPEG-4 AVC video encoder ASIC for real-time multimedia application.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.66-69
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• 2017

본 논문에서는 프레임율 향상 기법 (Frame Rate Up-Conversion, FRUC)에 사용되는 새로운 움직임 예측(motion estimation)알고리즘을 제시한다. 제안된 알고리즘은 단 방향 움직임 예측(unilateral motion estimation)에 의해 순방향 및 역방향의 움직임 벡터(motion vector)를 독립적으로 추정한다. 움직임 벡터를 찾은 후, weighted motion vector smoothing(WMVS)가 적용된다. 다음으로, 보간 프레임 (interpolated frame)의 관점에서 현재 블록의 인접 블록들의 모션 벡터들을 후보들로 사용하여 현재 블록과 가장 잘 일치하는 움직임 벡터를 찾는다. 그 후, 선택된 움직임 벡터를 현재 블록의 유도 움직임 벡터 (guidance motion vector)로 정한다. 그런 다음 motion vector shifting error 를 없애기 위해 motion vector refinement (MVR)가 진행된다. 마지막 단계에서는 각 움직임 벡터의 신뢰도를 계산하여 순방향 및 역방향 움직임 벡터 중 최종 움직임 벡터를 선택한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.817-823
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• 2009

This paper proposes a robust method for mapping of a caterpillar-type mobile robot which inherently has uncertainty in its modeling by compensating for the estimated pose error of the robot. In general, a caterpillar type robot is difficult to model, which results in inaccuracy in Simultaneous Localization And Mapping(SLAM). To enhance the robustness of the SLAM for a caterpillar-type mobile robot, we factorize the SLAM posterior, where we used particle filter to estimate the position of the robot and Extended Kalman Filter(EKF) to map the environment. The simulation results show the effectiveness and robustness of the proposed method for mapping.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.289-292
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• 2013

최근 많은 영상처리 연구자들 사이에서 마이크로소프트사의 실시간 깊이센서 '키넥트'가 상당한 관심을 받고 있다. '키넥트'는 실시간으로 깊이정보를 제공함과 동시에 별도의 센서를 부착하지 않고도 컴퓨터와의 인터렉션할 수 있는 가능성을 제공한다. 하지만 '키넥트'의 깊이영상은 홀 영역, 부정확한 경계, 낮은 해상도등의 많은 문제점을 지니고 있다. 이러한 부정확한 깊이 정보는 3차원 렌더링, 가상시점 영상 합성, 모션 인식 등에서 성능 저하를 야기한다. 따라서 본 논문에서는 깊이 정보 품질 향상기법에 관하여 깊이영상 신뢰도를 이용한 도메인 변환기반 해상도 상향 알고리듬을 제안한다. 정확하고 빠르게 홀 영역정보를 추정하기 위해 도메인 변환 기반의 경계 보존 필터링이 사용된다. 또한 다양한 깊이 영상의 노이즈를 효율적으로 제거하기 깊이 영상의 신뢰도를 이용한다. 실험결과를 통하여 제안하는 방법이 효율적으로 홀 영역을 채우고, 부정확한 경계를 제거하여 깊이 영상의 품질을 향상시키는 것을 확인할 수 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.792-799
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• 2008

Motion estimation is crucial in a remote control for its convenience or accuracy. Time delays, however, can occur in the problem because data communication is required through a network. In this paper, state estimation problem with uncertain time delayed measurements is addressed. In dynamic system with noise, after taking measurements, it often requires some time until that is available in the filter algorithm. Standard filters not considering this time delays cannot be used since the current measurement is related with a past state. These delayed measurements are solved with augmented extended Kalman filter, and the uncertainty of delayed time is also resolved based on an explicit formulation. The proposed method is analyzed and verified by simulations.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1842-1848
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• 2017

Recently, user motion estimation methods using various wearable devices have been actively studied. In this paper, we propose a motion estimation system using Myo, which is one of the wearable devices, using two Myo and their dependency relations. The estimated motion is used as a command for remotely controlling the robot. Myo's Orientation and EMG signals are used for motion estimation. These two type data sets are used complementarily to increase the accuracy of motion estimation. We design and implement the system according to the proposed method and analyze the results through experiments. As a result of comparison with previous studies, the accuracy of motion estimation can be improved by about 12.3%.

• Journal of Korea Multimedia Society
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• v.21 no.9
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• pp.1052-1061
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• 2018

In this paper, we propose a new algorithm that is robust against the effects of objects that are relatively unaffected by camera motion and can accurately detect camera motion even in high resolution images. First, for more accurate camera motion detection, a global motion filter based on entropy of a motion vector is used to distinguish the background and the object. A block matching algorithm is used to find exact motion vectors. In addition, a matched filter with the angle of the ideal motion vector of each block is used. Motion vectors including 4 kinds of diagonal direction, zoom in, and zoom out are added additionally. The experiment shows that the precision, recall, and accuracy of camera motion detection compared to the recent results is improved by 12.5%, 8.6% and 9.5%, respectively.