• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4C
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• pp.207-216
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• 2011

Motion compensated extrapolation (MCE) techniques show inferior performance compared to motion compensated interpolation techniques, since only past frames are used in MCE. MCE techniques are used for the reconstruction of corrupted frames, the up-conversion of frame rates and the generation of side information in the distributed video coding system. In this paper, the performance of various MCE techniques are evaluated and an efficient MCE technique using the forward and backward motion estimation is proposed. In the proposed technique, the present frame is extrapolated by averaging two frames which are generated by forward and backward motion estimation respectively. It is shown that the proposed method produces better PSNR results and less blocking phenomena than conventional methods.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.428-435
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• 2017

In this paper, we propose a novel motion field estimation algorithm for which a U-disparity map and forward-and-backward error removal are applied in a vehicular environment. Generally, a motion exists in an image obtained by a camera attached to a vehicle by vehicle movement; however, the obtained motion vector is inaccurate because of the surrounding environmental factors such as the illumination changes and vehicles shaking. It is, therefore, difficult to extract an accurate motion vector, especially on the road surface, due to the similarity of the adjacent-pixel values; therefore, the proposed algorithm first removes the road surface region in the obtained image by using a U-disparity map, and uses then the optical flow that represents the motion vector of the object in the remaining part of the image. The algorithm also uses a forward-backward error-removal technique to improve the motion-vector accuracy and a vehicle's movement is predicted through the application of the RANSAC (RANdom SAmple Consensus) to the previously obtained motion vectors, resulting in the generation of a motion field. Through experiment results, we show that the performance of the proposed algorithm is superior to that of an existing algorithm.

• Proceedings of the IEEK Conference
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• summer
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• pp.442-446
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• 2004

This paper propose scene change detection algorithm using coefficient of forward prediction macro-block, backward prediction macro-block, and intra-coded macro-block on getting motion estimation. Proposed method detect scene change with correlation according picture type forward two picture or forward and backward two picture on video sequences. Proposed algorithm is high accuracy and can detect all scene change on video, and detect to occur scene change on P, B, I-picture.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.471-478
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• 2012

A foot motion is estimated using an inertial sensor unit, which is installed on a shoe. The inertial sensor unit consists of 3 axis accelerometer and 3 axis gyroscopes. Attitude and position of a foot are estimated using an inertial navigation algorithm. To increase estimation performance, a smoother is used, where the smoother employs a forward and backward filter structure. An indirect Kalman filter is used as a forward filter and backward filter. A new combining algorithm for the smoother is proposed to combine a forward indirect Kalman filter and a backward indirect Kalman filter. Through experiments, the estimation performance of the proposed smoother is verified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2343-2352
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• 2015

In this paper, we propose novel motion field estimation method using U-disparity map and forward-backward error removal in vehicles environment. Generally, in an image obtained from a camera attached in a vehicle, a motion vector occurs according to the movement of the vehicle. but this motion vector is less accurate by effect of surrounding environment. In particular, it is difficult to extract an accurate motion vector because of adjacent pixels which are similar each other on the road surface. Therefore, proposed method removes road surface by using U-disparity map and performs optical flow about remaining portion. forward-backward error removal method is used to improve the accuracy of the motion vector. Finally, we predict motion of the vehicle by applying RANSAC(RANdom SAmple Consensus) from acquired motion vector and then generate motion field. Through experimental results, we show that the proposed algorithm performs better than old schemes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.483-487
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• 1996

We consider the estimation of the position and orientation of 6 DOF motion bed (Stewart platform) from the measured cylinder length. The solution of forward kinematics is not solved yet as a useful realtime application tool because of the complity of the equation with multiple solutiple solutions. Hence we suggest an algorithm for the estimation of forward kinematics solution using Luenberger observer withnonlinear error correction term. The Luenberger observer withlinear model shows that the estimation error does not go to zero in steadystate due to the linearization error of the dynamic model. Hence the linear observer is modified using nonlinear measurement error equation and we prove thd practical stability of the estimation error dynamics of the proposed observer using lyapunov function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1109-1122
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• 1996

Generally, moving images contain the various components in motions, which reange from a static object and background to a fast moving object. To extract the accurate motion parameters, we must consider the various motions. That requires a wide search egion in motion estimation. The wide search, however, causes a high computational complexity. If we have a few knowledge about the motion direction and magnitude before motion estimation, we can determine the search location and search window size using the already-known information about the motion. In this paper, we present a local adaptive motion estimation approach that predicts a block motion based on spatio-temporal neighborhood blocks and adaptively defines the search location and search window size. This paper presents a technique for reducing computational complexity, while having high accuracy in motion estimation. The proposed algorithm is introduced the forward and backward projection techniques. The search windeo size for a block is adaptively determined by previous motion vectors and prediction errors. Simulations show significant improvements in the qualities of the motion compensated images and in the reduction of the computational complexity.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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• pp.1632-1642
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• 1994

The Stewart Platform is a six-degree-of-freedom in-parallel-actuated manipiulator mechanism. The kinematic behavior of parallel mechanisms shows inverse characteristics as compared that of serial mechanisms; i.e, the inverse kinematic problem of Stewart Platform is straightforward, but no closed form solution of the forward kinematic problem has been previously presented. Thus it is difficult to calculate the 6 DOF displacement of the platform from the measured lengths of the six actuators in real time. Here, a real-time estimation algorithm which solves the Stewart Platform kinematic problem is proposed and tested through computer simulations and experiments. The proposed algorithm shows stable convergence characteristics, no estimation errors in steady state and good estimation performance with higher sampling rate. In experiments it is shown that the estimation result is the same as that of simulation even in the presence of measurement noise.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.1-8
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• 2012

Video FRUC (Frame Rate Up Conversion) has been a technique of great interest due to its diversified applications in consumer electronics. Most advanced FRUC algorithms adopt a motion interpolation technique to determine the motion vector field of interpolated frames. But, in some applications, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame is reconstructed. For this aim, this paper proposes a distortion estimation for motion compensated interpolation frame using frame-adaptive distortion estimation. The proposed method is applied for the symmetric motion estimation and compensated scheme and then analyzed by three different approaches, that is, forward estimation, backward estimation and adaptive bi-directional estimation schemes. Through computer simulations, it is shown that the proposed bi-directional estimation method outperforms others and can be effectively applied for FRUC.

• Journal of Broadcast Engineering
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• v.14 no.1
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• pp.59-69
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• 2009

A bi-directional symmetric prediction technique has been developed to improve coding efficiency of B-slice and to reduce the computational complexity required to estimate two motion vectors. On the contrary to the conventional bi-directional mode which encodes both forward and backward motion vectors, it only encodes a single forward motion vector, and the missing backward motion vector is derived in a symmetric way from the forward motion vector using temporal distance between forward/backward reference frames to and from the current B picture. Since the backward motion vector is derived from the forward motion vector, it can halve the computational complexity for motion estimation, and also reduces motion vector data to encode. This technique always derives the backward motion vector from the forward motion vector, however, there are cases when the forward motion vector is better to be derived from the backward motion vector especially in scene changes. In this paper, we generalize the idea of the symmetric coding with forward motion vector coding, and propose a new symmetric coding with backward motion vector coding and adaptive selection between the conventional symmetric mode and the proposed symmetric mode based on rate-distortion optimization.