• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2209-2215
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• 2004

In the present research work, an automated machine vision system and a new algorithm to interpret the inspection data has been developed. In the past, the control of tolerance of front-chassis-module was done manually. In the present work a machine vision system and required algorithm was developed to carryout dimensional evaluation automatically. The present system is used to verify whether the automobile front-chassis-module is within the tolerance limit or not. The directional ability parameters related with front-chassis-module such as camber, caster, toe and king-pin angle are also determined using the present algorithm. The above mentioned parameters are evaluated by the pose of interlinks in the assembly of an automobile front-chassis-module. The location of ball-joint center is important factor to determine these parameters. A method to determine the location of ball-joint center using geometric features is also suggested in this paper. In the present work a 3-D best fitting method is used for determining the relationship between nominal design coordinate system and the corresponding feature coordinate system.

• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1212-1220
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• 2013

The human coagulation factor XI (FXI) is a serine protease that plays a significant role in blocking of the blood coagulation cascade as an attractive antithrombotic target. Selective inhibition of FXIa (an activated form of factor XI) disrupts the intrinsic coagulation pathway without affecting the extrinsic pathway or other coagulation factors such as FXa, FIIa, FVIIa. Furthermore, targeting the FXIa might significantly reduce the bleeding side effects and improve the safety index. This paper reports on a docking-based three dimensional quantitative structure activity relationship (3D-QSAR) study of the potent FXIa inhibitors, the chloro-phenyl tetrazole scaffold series, using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Due to the characterization of FXIa binding site, we classified the alignment of the known FXIa inhibitors into two groups according to the docked pose: S1-S2-S4 and S1-S1'-S2'. Consequently, highly predictive 3D-QSAR models of our result will provide insight for designing new potent FXIa inhibitors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.396-398
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• 2009

Scoliosis is a three-dimensional deformity caused by lateral curvature of the spine. The existing braces used to correct the posture were some drawbacks such as inconvenience, tightness as well as unfitness to wear. In this study, we devised a posture guidance system in order to monitor a posture continuously and lead to pose correctly and a new method fur measuring a Cobb's angle value in third dimension based on two 3-axis accelerometers. As a result, the correlation coefficients between desired and measured angles were and standard error between desired and measured angles were 0.99, 1.32(x-axis), 0.99 and 1.10(y-axis), respectively. The devised system showed good potential for the optimal posture guide and an early detection of scoliosis.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.50-50
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• 2000

In this paper, a new measuring system is :proposed which can measure the fine 6-DOF displacement of rigid bodies. Its measurement principle is based on detection of laser beam reflected from a specially fabricated mirror that looks like a triangular pyramid having an equilateral cross-sectional shape. The mirror has three lateral reflective surfaces inclined 45$^{\circ}$ to its bottom surface. We call this mirror 3-facet mirror. The 3-facet mirror is mounted on the object whose 6-DOF displacement is to be measured. The measurement is operated by a laser-based optical system composed of a 3-facet mirror, a laser source, three position-sensitive detectors(PSD). In the sensor system, three PSDs are located at three corner points of a triangular formation, which is an equilateral triangular formation tying parallel to the reference plane. The sensitive areas of three PSDs are oriented toward the center point of the triangular formation. The object whose 6-DOF displacement is to be measured is situated at the center with the 3-facet mirror on its top surface. A laser beam is emitted from the laser source located at the upright position and vertically incident on the top of the 3-fatcet mirror. Since each reflective facet faces toward each PSD, the laser beam is reflected at the 3-facet mirror and splits into three sub-beams, each of which is reflected from the three facets and finally arrives at three PSDs, respectively. Since each PSD is a 2-dimensional sensor, we can acquire the information on the 6-DOF displacement of the 3-facet mirror. From this principle, we can get 6-DOF displacement of any object simply by mounting the 3-facet mirror on the object. In this paper, we model the relationship between the 6-DOF displacement of the object and the outputs of three PSDs. And, a series of simulations are performed to demonstrate the effectiveness of the proposed method. The simulation results show that the proposed sensing system can be an effective means of obtaining 3-dimensional position and orientation of arbitrary objects.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1045-1050
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• 2005

This paper describes a design experience of a low-cost 6 DOF spatial tracker system where relative low accuracy and relatively long ranges, wireless communication will be achieved by means of low cost accelerometers and gyros with contemporary microprocessor. However, there are two key problems; one is the bias drift problem and the other is that single or double integration of acceleration signal suffers not only from noise but also from nonlinear effects caused by gravity. To be specific, beginning and stopping of hand motions needs to be accurately detected to initiate and terminate integration process to get position and pose of the hand from accelerometer and gyro signals, since errors due to noise and/or hand-shaking motions accumulated by integration processes. Several experimental results are shown to validate our proposed algorithms.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.519-532
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• 2021

Recently, in various fields such as games, movies, and animation, content that uses motion capture to build body models and create characters to express in 3D space is increasing. Studies are underway to generate animations using RGB-D cameras to compensate for problems such as the cost of cinematography in how to place joints by attaching markers, but the problem of pose estimation accuracy or equipment cost still exists. Therefore, in this paper, we propose a system that inputs RGB images into a joint estimation network and converts the results into 3D data to create FBX format animations in order to reduce the equipment cost required for animation creation and increase joint estimation accuracy. First, the two-dimensional joint is estimated for the RGB image, and the three-dimensional coordinates of the joint are estimated using this value. The result is converted to a quaternion, rotated, and an animation in FBX format is created. To measure the accuracy of the proposed method, the system operation was verified by comparing the error between the animation generated based on the 3D position of the marker by attaching a marker to the body and the animation generated by the proposed system.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.265-275
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• 2015

This paper describes extraction methods of five different types of geometrical features (line, arc, corner, polynomial curve, NURBS curve) from the obtained raw data by using a two-dimensional laser range finder (LRF). Natural features with their covariance matrices play a key role in the realization of feature-based simultaneous localization and mapping (SLAM), which can be used to represent the environment and correct the pose of mobile robot. The covariance matrices of these geometrical features are derived in detail based on the raw sensor data and the uncertainty of LRF. Several comparison are made and discussed to highlight the advantages and drawbacks of each type of geometrical feature. Finally, the extracted features from raw sensor data obtained by using a LRF in an indoor environment are used to validate the proposed extraction methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.114-117
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• 2009

A new video inpainting algorithm is proposed for removing unwanted objects or error of sources from video data. In the first step, the block bundle is defined by the motion information of the video data to keep the temporal consistency. Next, the block bundles are arranged in the 3-dimensional graph that is constructed by the spatial and temporal correlation. Finally, we pose the inpainting problem in the form of a discrete global optimization and minimize the objective function to find the best temporal bundles for the grid points. Extensive simulation results demonstrate that the proposed algorithm yields visually pleasing video inpainting results even in a dynamic scene.