• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.679-684
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• 1993

This article presents an approach to estimate the general 3D motion of a polyhedral object using multiple, sensory data some of which may not provide sufficient information for the estimation of object motion. Motion can be estimated continuously from each sensor through the analysis of the instantaneous state of an object. We have introduced a method based on Moore-Penrose pseudo-inverse theory to estimate the instantaneous state of an object. A linear feedback estimation algorithm is discussed to estimate the object 3D motion. Then, the motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown object. The techniques of multisensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.64-72
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• 1996

In this paper, we propose predictive motion estimatin algorithm which can predict motion without additional side information considering spatio-tempral correlatio of motion vector. This method performs motion prediction of current block using correlation of the motion vector for two spatially adjacent blocks and a temporally adjacent block. Form predicted motion, the position of searhc area is determined. Then in this searhc area, we estimate motion vector of current block using block matching algoirthm. Considering spatial an temporal correlation of motion vector, the proposed method can predict motion precisely much more. Especially when the motion of objects is rapid, this method can estimate motion more precisely without reducing block size or increasing search area. Futhrmore, the proposed method has computation time the same as conventional block matching algorithm. And as it predicts motion from adjacent blocks, it does not require additional side information for adjacent block. Computer simulation results show that motion estimation of proposed method is more precise than that of conventioanl method.

• Journal of Mechanical Science and Technology
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• v.16 no.11
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• pp.1379-1394
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• 2002

In case of a single input single output (SISO) system with a nonlinear term, a signal compression method is useful to identify a system because the equivalent impulse response of linear part from the system can be extracted by the method. However even though the signal compression method is useful to estimate uncertain parameters of the system, the method cannot be directly applied to a unique system with hysteresis characteristics because it cannot estimate all of the two different dynamic properties according to its motion direction. This paper proposes a signal compression method with a pre-processor to identify a unique system with two different dynamics according to its motion direction. The pre-processor plays a role of separating expansion and retraction properties from the system with hysteresis characteristics. For evaluating performance of the proposed approach, a simulation to estimate the assumed unknown parameters for an arbitrary known model is carried out. A motion platform with several single-rod cylinders is a representative unique system with two different dynamics, because each single-rod cylinder has expansion and retraction dynamic properties according to its motion direction. The nominal constant parameters of the motion platform are experimentally identified by using the proposed method. As its application, the identified parameters are applied to a design of a sliding mode controller for the simulator.

• ETRI Journal
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• v.33 no.2
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• pp.259-266
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• 2011

We present a new method for an automated markerless system to describe, analyze, and classify human gait motion. The automated system consists of three stages: I) detection and extraction of the moving human body and its contour from image sequences, ii) extraction of gait figures by the joint angles and body points, and iii) analysis of motion parameters and feature extraction for classifying human gait. A sequential set of 2D stick figures is used to represent the human gait motion, and the features based on motion parameters are determined from the sequence of extracted gait figures. Then, a k-nearest neighbor classifier is used to classify the gait patterns. In experiments, this provides an alternative estimate of biomechanical parameters on a large population of subjects, suggesting that the estimate of variance by marker-based techniques appeared generous. This is a very effective and well-defined representation method for analyzing the gait motion. As such, the markerless approach confirms uniqueness of the gait as earlier studies and encourages further development along these lines.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.8
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• pp.1423-1443
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• 2011

Background compensation plays an important role in detecting and isolating object motion in visual tracking. Here, we propose a Genetic Hough Transform, which combines the Hough Transform and Genetic Algorithm, as a method for eliminating background motion. Our method can handle cases in which the background may contain only a few, if any, feature points. These points can be used to estimate the motion between two successive frames. In addition to dealing with featureless backgrounds, our method can successfully handle motion blur. Experimental comparisons of the results obtained using the proposed method with other methods show that the proposed approach yields a satisfactory estimate of background motion.

• The Journal of Korea Robotics Society
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• v.5 no.2
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• pp.85-92
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• 2010

In this paper, we propose a digital image stabilization technique using edge detection and Lucas-Kanade optical flow in order to minimize the motion of the shaken image. The accuracy of motion estimation based on block matching technique depends on the size of search window, which results in long calculation time. Therefore it is not applicable to real-time system. In addition, since the size of vector depends on that of block, it is difficult to estimate the motion which is bigger than the block size. The proposed method extracts the trust region using edge detection, to estimate the motion of some critical points in trust region based on Lucas-Kanade optical flow algorithm. The experimental results show that the proposed method stabilizes the shaking of motion image effectively in real time.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.181-197
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• 2003

Notion Compensation(MC) technique using Sub-Band Coding with the hierarchical structure is efficient to estimate real motion. In the hierarchical pyramid method, low-band MC pyramid method is popular, where the upper layer estimate the glover motion and next lower layer estimate the local motion. The low-band MC pyramid scheme has two problems. First, because the quantization errors at lower layer are accumulated when using coding and quantizing, it is impossible to search the exact Motion Vector(MV) Second, because of the top-down search problem in the hierarchical structure, MV mismatch in upper layer causes serious MV in lower layer So. we propose new hierarchical MC pyramid method based on edge classification. In this Paper, we show that the performance of proposed Pass-band motion compensation pyramid technique is better than low-band motion compensation pyramid. Also, in the pyramid motion estimation, we propose initial MV estimation scheme based on the edge-pattern classification. As a result, we find that PSNR was increased.

• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.618-631
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• 2000

In this paper, we suggest the method of 3D facial synthesis using the motion of 2D facial images. We use the optical flow-based method for estimation of motion. We extract parameterized motion vectors using optical flow between two adjacent image sequences in order to estimate the facial features and the facial motion in 2D image sequences. Then, we combine parameters of the parameterized motion vectors and estimate facial motion information. We use the parameterized vector model according to the facial features. Our motion vector models are eye area, lip-eyebrow area, and face area. Combining 2D facial motion information with 3D facial model action unit, we synthesize the 3D facial model.

• Korean Journal of Applied Biomechanics
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• v.12 no.1
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• pp.125-133
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• 2002

The purpose of this study was to develop a method for estimating 3-D coordinates of lower trunk muscles using orientation angles during a motion. Traditional 3-D motion analysis system with DLT technique was used to track down the locations of eight reference markers which were attached on the back of the subject. In order to estimate the orientations of individual lumbar vertebrae and musculoskeletal parameters of the lower trunk muscle, the rotation matrix of the middle trunk reference frame relative to the lower trunk reference frame was determined and the angular locations of individual lumbar vertebrae were estimated by partitioning the orientation angles (Cardan angles) that represent the relative angles between the rotations of the middle and lower trunks. When the orientation angles of individual intervertebral joints were known at a given instant, the instantaneous coordinates of the origin and insertion for all selected muscles relative to the L5 local reference frame were obtained by applying the transformation matrix to the original coordinates which were relative to a local reference frame (S1, L4, L3, L2, or L1) in a rotation sequence about the Z-, X- and Y-axes. The multiplication of transformation matrices was performed to estimate the geometry and kinematics of all selected muscles. The time histories of the 3-D coordinates of the origin and insertion of all selected muscles relative to the center of the L4-L5 motion segment were determined for each trial.

• Journal of Biomedical Engineering Research
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• v.30 no.3
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• pp.241-246
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• 2009

Since inappropriate muscle forces mean that people cannot perform some activities related to roles of the muscle, muscle forces have been considered as an important parameter in clinic. Therefore, many methods have been introduced to estimate muscle forces indirectly. One of the methods is muscle tissue dynamics and it is widely used in commercial softwares including musculoskeletal model, such as SIMM. They, however, need motion data captured from 3-dimensional motion analysis system. In this study, we introduced an algorithm to estimate muscle forces in real-time by using joint angles. The heel-rise movements were performed for a normal with 3-dimensional motion analysis system, EMG measurement system, and electrogoniometers. Joint angles obtained from electrogoniometers and EMG signals were used to estimate muscle forces. Simulation was performed to find muscle forces using motion data which was imported into musculoskeletal software. As the results, muscle lengths and forces from the developed algorithm were similar to those from commercial software in pattern. Results of this study would be helpful to implement a tool to calculate reasonable muscle forces in real-time.