• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1205-1211
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• 2008

This paper describes methods to estimate 3-D position of target with respect to reference frame through monocular image from unmanned aerial vehicle (UAV). 3-D position of target is used as information for surveillance, recognition and attack. In this paper. 3-D position of target is estimated to make guidance and control law, which can follow target, user interested. It is necessary that position of target is measured in image to solve 3-D position of target. In this paper, kalman filter is used to track and output position of target in image. Estimation of target's 3-D position is possible using result of image tracking and information of UAV and camera. To estimate this, two algorithms are used. One is methode from arithmetic derivation of dynamics between UAV, carmer, and target. The other is LPV (Linear Parametric Varying). These methods have been run on simulation, and compared in this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.296-300
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• 1996

This paper presents 3D position estimation algorithm with the images of 2D vision sensors which issues Red Laser Slit light and recieves the line images. Since the sensor usually measures 2D position of corner(or edge) of a body and the measured point is not fixed in the body, the additional information of the corner(or edge) is used. That is, corner(or edge) line is straight and fixed in the body. For the body which moves in a plane, the Transformation matrix between the body coordinate and the reference coordinate is analytically found. For the 3D motion body, linearization technique and least mean squares method are used.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.133-142
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• 1999

We introduce the algorithms of 2-D and 3-D position estimation using 2-D vision sensors. The sensors used in this research issue red laser slit light to the body. So, it is very convenient to obtain the coordinates of corner point or edge in sensor coordinate. Since the measured points are normally not fixed in the body coordinate, the additional conditions, that corner lines or edges are straight and fixed in the body coordinate, are used to find out the position and orientation of the body. In the case of 2-D motional body, we can find the solution analytically. But in the case of 3-D motional body, linearization technique and least mean squares method are used because of hard nonlinearity.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.23-35
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• 2002

Gaze detection is to locate the position on a monitor screen where a user is looking. In our work, we implement it with a computer vision system setting a single camera above a monitor and a user moves (rotates and/or translates) his face to gaze at a different position on the monitor. To detect the gaze position, we locate facial region and facial features(both eyes, nostrils and lip corners) automatically in 2D camera images. From the movement of feature points detected in starting images, we can compute the initial 3D positions of those features by camera calibration and parameter estimation algorithm. Then, when a user moves(rotates and/or translates) his face in order to gaze at one position on a monitor, the moved 3D positions of those features can be computed from 3D rotation and translation estimation and affine transform. Finally, the gaze position on a monitor is computed from the normal vector of the plane determined by those moved 3D positions of features. As experimental results, we can obtain the gaze position on a monitor(19inches) and the gaze position accuracy between the computed positions and the real ones is about 2.01 inches of RMS error.

• Journal of Sensor Science and Technology
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• v.32 no.4
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• pp.232-237
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• 2023

This paper presents a system for estimating the position of a magnet using a magnetic sensor. An algorithm is presented to analyze the waveform and output voltage values of the magnetic field generated at each position when the magnet moves and to estimate the position of the magnet based on the analyzed data. Here, the magnet is sufficiently small to be inserted into a blood vessel and has a micro-magnetic field of hundreds of nanoteslas owing to the small size and shape of the guide wire. In this study, a highly sensitive magneto-impedance (MI) sensor was used to detect these micro-magnetic fields. Nine MI sensors were arranged in a 3×3 configuration to detect a magnetic field that changes according to the position of the magnet through the MI sensor, and the voltage value output was polynomially regressed to specify a position value for each voltage value. The accuracy was confirmed by comparing the actual position value with the estimated position value by expanding it from a 1D straight line to a 3D space. Additionally, we could estimate the position of the magnet within a 3% error.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.832-836
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• 1988

"Motion Stereo" is quite useful for visual guidance of the robot, but most range finding algorithms of motion stereo have suffered from poor accuracy due to the quantization noise and measurement error. In this paper, 3-D position estimation and refinement scheme is proposed, and its performance is discussed. The main concept of the approach is to consider the entire frame sequence at the same time rather than to consider the sequence as a pair of images. The experiments using real images have been performed under following conditions : hand-held camera, static object. The result demonstrate that the proposed nonlinear least-square estimation scheme provides reliable and fairly accurate 3-D position information for vision-based position control of robot. of robot.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.532-539
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• 2018

A position estimation technique is presented for a missile using high-resolution range profiles obtained by three wideband radars. Radar measures a target range using a reflected signal from the surface of a missile. However, it is difficult to obtain the range between the radar and the origin of the missile. For this reason, the interior angle between the moving missile and tracking radar is calculated, and a compensated range between surface of the missile and its origin is added to the tracking range of the radar. Therefore, position estimation of a missile can be achieved by using three total ranges from each radar to the origin of the missile. To verify the position estimation of the missile, electromagnetic numerical analysis software was used to prove the compensated range according to the flight position. Moreover, a wideband radar operating at 500-MHz bandwidth was applied, and its range profile was used for the position estimation of a missile.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.12
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• pp.1002-1016
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• 1990

This paper described design and implementation of stereo vision system, and also, proposed method for displacement estimation of 3-D moving object using this system. The extraction of moving object is obtained by difference image algorithm. Geometrical position of 3-D moving object is calculated form the mapping of center area of two's 2-D object. 3-D coordinate position produced space depth, moving velociity, distance, moving track and proved displacement estimation of 3-D moving object.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.158-163
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• 2009

In recent years, there has been increased interest in characterizing and extracting 3D information from 2D images for human tracking and identification. In this paper, we propose a single view-based framework for robust estimation of height and position. In the proposed method, 2D features of target object is back-projected into the 3D scene space where its coordinate system is given by a rectangular marker. Then the position and the height are estimated in the 3D space. In addition, geometric error caused by inaccurate projective mapping is corrected by using geometric constraints provided by the marker. The accuracy and the robustness of our technique are verified on the experimental results of several real video sequences from outdoor environments.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.171-174
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• 2003

In this paper, we propose the method of stereo images composition using adaptive dense disparity estimation. For the correct composition of stereo image and 3D virtual object, we need correct marker position and depth information. The existing algorithms use position information of markers in stereo images for calculating depth of calibration object. But this depth information may be wrong in case of inaccurate marker tracking. Moreover in occlusion region, we can't know depth of 3D object, so we can't composite stereo images and 3D virtual object. In these reasons, the proposed algorithm uses adaptive dense disparity estimation for calculation of depth. The adaptive dense disparity estimation is the algorithm that use pixel-based disparity estimation and the search range is limited around calibration object.