• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.321-327
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• 2005

A MVC (Multi-view Video Coding) method, which uses both an illumination change-adaptive ME (Motion Estimation)/DC (Motion Compensation) and a 2D (Dimensional) direct mode, is proposed. Firstly, a new SAD (Sum of Absolute Difference) measure for ME/MC is proposed to compensate the Luma pixel value changes for spatio-temporal motion vector prediction. Illumination change-adaptive (ICA) ME/MC uses the new SAD to improve both MV (Motion Vector) accuracy and bit saving. Secondly, The proposed 2D direct mode that can be used in inter-view prediction is an extended version of the temporal direct mode in MPEG-4 AVC. The proposed MVC method obtains approximately 0.8dB PSNR (Peak Signal-to-Noise Ratio) increment compared with the MPEG-4 AVC simulcast coding.

• ETRI Journal
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• v.43 no.3
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• pp.538-548
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• 2021

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.

• Journal of IKEEE
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• v.17 no.1
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• pp.36-43
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• 2013

This paper proposes a motion estimation algorithm with run-time adaptive workload control. It has optimized performance within limited hardware resources while guaranteeing hard realtime operation. It performs maximum searches within hard realtime constraints, since it determines search steps and workload adaptively. It reduces the hardware size to 1/4~1/400 of conventional algorithms, while its PSNR degradation is only 0.02~0.44 dB. It can be easily applied to most conventional fast algorithms, so it is useful to design realtime encoder chips.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.57-65
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• 2007

In the fast block matching algorithm. search patterns of different shapes or sizes and the distribution of motion vectors have a large impact on both the searching speed and the image qualify. In this paper, we propose a new fast block matching algorithm using the flat-hexagon search pattern that ate solved disadvantages of the diamond pattern search algorithm(DS) and the hexagon-based search algorithm(HEXBS). Our proposed algorithm finds mainly the motion vectors that not close to the center of search window using the flat-hexagon search pattern. Through experiments, compared with the DS and HEXBS, the proposed f)at-hexagon search algorithm(FHS) improves about $0.4{\sim}21.3%$ in terms of average number of search point per motion vector estimation and improves about $0.009{\sim}0.531dB$ in terms of PSNR(Peak Signal to Noise Ratio).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.302-320
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• 2017

In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.

• International Journal of Ocean System Engineering
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• v.2 no.4
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• pp.223-232
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• 2012

For the accurate estimation of the position and orientation of a UUV (unmanned underwater vehicle), a low-cost AHRS (attitude heading reference system) was developed using a low-cost IMU (inertial measurement unit) sensor which provides information on the 3D acceleration, 3D turning rate and 3D earth-magnetic field data in the object coordinate system. The main hardware system is composed of an IMU sensor (ADIS16405) and TMS320F28335, which is coded with an extended kalman filter algorithm with a 50-Hz sampling frequency. Through an experimental gimbal device, good estimation performance for the pitch, roll, and yaw angles of the developed AHRS was verified by comparing to those of a commercial AHRS called the MTi system. The experimental results are here presented and analyzed.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1029-1032
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• 1999

Muscle based face image synthesis is one of the most realistic approach to realize life-like agent in computer. Facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue element by contraction of each muscle strength, so the combination of each muscle parameter decide a specific facial expression. Now each muscle parameter is decided on trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific face image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D marker movement using neural network. This also 3D motion estimation from 2D point or flow information in captered image under restriction of physics based face model.

• Journal of the Korean Society of Radiology
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• v.12 no.1
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• pp.115-122
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• 2018

The success of the total hip arthroplasty is revealed as initial stability, range of motion, and long term pain, etc. Depending upon choice of implantation options such as femoral neck offset, diameter of the femoral head, the lateral opening tilt. Especially the impingement between femoral head component and acetabular cup limits the range of motion of the hip. In this sense, estimation or evaluation of the range of motion before and after the total hip arthroplasty is important. This study provides the details of a computer simulation technique for the hip range of motion of intact hip as well as arthroplasty. The suggested method defines the hip rotation center and rotation axes for flexion and abduction, respectively. The simulation uses CT-based reconstructed 3D models and an STL treating software. The abduction angle of the hip is defined as the superolateral rotation angle from sagittal plane. The flexion angle of the hip is defined as the superoanterior angle from the coronal plane. The maximum abduction angle is found as the maximum rotation angle by which the femoral head can rotate superolaterally about the anterior-posterior axis without impingement. The maximum flexion angle is found as the maximum rotation angle by which the femoral head can rotate superoanteriorly about the medial-lateral axis without impingement. Compared to the normal hip, the total hip replacement hip showed decreased abduction by 60 degrees and decreased flexion by 4 degrees. This measured value implies that the proposed measurement technique can make surgeons find a modification of increase in the femoral neck offset or femoral head, to secure larger range of motion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• International journal of advanced smart convergence
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• v.5 no.4
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• pp.1-9
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• 2016

In order to reconstruct a 3D model in video sequences, to select key frames that are easy to estimate a geometric model is essential. This paper proposes a method to easily extract informative frames from a handheld video. The method combines selection criteria based on appropriate-baseline determination between frames, frame jumping for fast searching in the video, geometric robust information criterion (GRIC) scores for the frame-to-frame homography and fundamental matrix, and blurry-frame removal. Through experiments with videos taken in indoor space, the proposed method shows creating a more robust 3D point cloud than existing methods, even in the presence of motion blur and degenerate motions.