• The KIPS Transactions:PartB
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• v.14B no.6
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• pp.437-444
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• 2007

The objective of this paper is to accommodate the existing motion-capture-based animation to small memories and low computing powers of mobile embedded systems. To use efficiently memories, we propose a paradigm in which a motion capture database is compressed on a PC and so-compressed motion capture database is decompressed little by little on mobile embedded systems and the decompressed data are eliminated right after used. As a compression method for this paradigm we propose an approach that compresses captured motion rendering parameters using a polynomial function fitting method. To enhance its performance we also propose an optimization method for the degree of the polynomial fitting function. Using so-obtained compression method we demonstrate motion-capture-based animation on commercial mobile embedded systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.509-517
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• 2001

Full-search algorithm requires large amount of computation which causes time delay or very complex hardware architecture for real time implementation. In this paper, we propose a fast motion estimator based on bit-plane matching, which reduce the computational complexity and the hardware cost. In the proposed motion estimator, the conventional motion estimation algorithms are applied to the binary images directly extracted from the video sequence. Furthermore, in the proposed VLSI motion estimator, we employ a Pair of processing cores that calculate the motion vector continuously By controlling the data flow in a systolic fashion using the internal shift registers in the processing cores, we avoid using SRAM (local memory) so that we remove the time overhead for accessing the local memory and adopt lower-cost fabrication technology. We modeled and tested the proposed motion estimator in VHDL, and then synthesized the whole system which has been integrated in a 0.6-$\mu$m triple-metal CMOS chip of size 8.15 X 10.84$\textrm{mm}^2$.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.53-62
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• 2004

In this study, an improved post-processing technique for correcting MRI artifact due to the unknown respiratory motion in the imaging plane is presented. Respiratory motion is modeled by a two-Dimensional linear expending-shrinking movement. Assuming that the body tissues are incompressible fluid like materials, the proton density per unit volume of the imaging object is kept constant. According to the introduced model, respiratory motion imposes phase error, non-uniform sampling and amplitude modulation distortions on the acquired MRI data. When the motion parameters are known or can be estimatead a reconstruction algorithm based on biliner superposition method was used to correct the MRI artifact. In the case of motion parameters are unknown, first, the spectrum shift method is applied to find the respiratory fluctuation function, x directional expansion coefficient and x directional expansion center. Next, y directional expansion coefficient and y directional expansion center are estimated by using the minimum energy method. Finally, the validity of this proposed method is shown to be effective by using the simulated motion images.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.5
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• pp.58-65
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• 2007

As multimedia portable devices become popular, the amount of computation for processing data including video compression has significantly increased. Various researches for low power consumption of the mobile devices and real time processing have been reported. Motion Estimation is responsible for 67% of H.264 encoder complexity. In this research, a new circuit is designed for motion estimation. The new circuit uses motion prediction based on approximate SAD, Alternative Row Scan (ARS), DAU, and FDVS algorithms. Our new method can reduce the amount of computation by 75% when compared to multi-frame motion estimation suggested in JM8.2. Furthermore, optimal number and size of reference frame blocks are determined to reduce computation without affecting the PSNR. The proposed Motion Estimation method has been verified by using the hardware and software Co-Simulation with iPROVE. It can process 30 CIF frames/sec at 50MHz.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.189-194
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• 2016

Recently, there is increasing demand for image processing technology while activated the use of equipments such as camera, camcorder and CCTV. In particular, research and development related to 3D image technology using the depth camera such as Kinect sensor has been more activated. Kinect sensor is a high-performance camera that can acquire a 3D human skeleton structure via a RGB, skeleton and depth image in real-time frame-by-frame. In this paper, we develop a system. This system captures the motion of a 3D human skeleton structure using the Kinect sensor. And this system can be stored by selecting the motion file format as trc and bvh that is used for general purposes. The system also has a function that converts TRC motion captured format file into BVH format. Finally, this paper confirms visually through the motion capture data viewer that motion data captured using the Kinect sensor is captured correctly.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.3
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• pp.231-238
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• 2017

In this paper, we propose a method of analyzing exercise behavior to increase health care and exercise effect in personal fitness. In this study, a user wears a band-shaped acceleration sensor, an angular velocity sensor, and a motion sensor equipped with a geomagnetic module. Using the technique presented in this paper, we analyzed the motion of three resistive exercises which is consistent with previous studies. We have acquired a technique for processing personalized exercise information from the data generated in the resistive exercise situation.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.537-641
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• 2007

In this paper, motion analysis algorithm is presented for home surveillance of the aged who lives alone. For the first step, we acquire images from a camera. To enhance the image, we use median filtering and binarize it to reduce processing time. And then morphological operations are performed to remove small blobs and small holes. At the forth step, blobs are analysed to extracts tor foreground region. Then, motions are predicted from these images by using optical tlow technique, and the predicted motion data are refined by comparing our cardboard models so as to judge behavior pattern.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.857-860
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• 2005

Several algorithms have been developed for the data processing of spotlight synthetic aperture radar (SAR). In particular, the range migration algorithm (RMA) does not assume that illuminating wavefronts are planar. Also, a high resolution image can be obtained by the RMA. This paper introduces an extension of the original RMA to enable a more efficient airborne SAR data processing. We consider more general motion and scene than the original RMA. The presented formulation is analyzed by using the principle of the stationary phase. Finally, the extended algorithm is tested with numerical simulations using the pulsed spotlight SAR.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.655-660
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• 2000

Animation technique from the PIV database is particularly emphasized to give macroscopic and quantitative description of complex flow fields. This paper shows animation construction and application example for the post-processing of PIV data. As examples, first case is a pitching airfoil immersed in tree surface water circulating tunnel. Second case is a wake of a model-ship. Third case of PIV data is a large scale surface flow field. Obtained images are processed in time sequence by PIV exclusive routines where an efficient and reliable cross correlation algorithm is included for vector identification. All. animation Jobs are implemented completely on single personal computer environment. Compressed digital images are obtained initially by Motion-JPEG board and various An files are finally obtained through graphic processes.