• Journal of Biomedical Engineering Research
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• v.12 no.1
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• pp.49-56
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• 1991

In this paper, a method for camera position estimation in gasher using elechoendoscopic image sequence is proposed. In orders to obtain proper image sequences, the gasser in divided into three sections. It Is presented thats camera position modeling for 3D information extvac lion and image distortion due to the endoscopic lenses is corrected. The feature points are represented with respect to the reference coordinate system below 10 percents error rate. The faster distortion correction algorithm is proposed in this paper. This algorithm uses error table which is faster than coordinate transform method using n -th order polynomials.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.494-504
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• 2004

The researches about gaze detection have been much developed with many applications. Most previous researches only rely on image processing algorithm, so they take much processing time and have many constraints. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye's movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.2 cm of RMS error.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1865-1873
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• 2017

The feature-based visual SLAM requires 3D positions for the extracted feature points to perform 3D-2D motion estimation. LiDAR can provide reliable and accurate 3D position information with low computational burden, while stereo camera has the problem of the impossibility of stereo matching in simple texture image region, the inaccuracy in depth value due to error contained in intrinsic and extrinsic camera parameter, and the limited number of depth value restricted by permissible stereo disparity. However, the sparsity of LiDAR data may increase the inaccuracy of motion estimation and can even lead to the result of motion estimation failure. Therefore, in this paper, we propose three interpolation methods which can be applied to interpolate sparse LiDAR data. Simulation results obtained by applying these three methods to a visual odometry algorithm demonstrates that the selective bilinear interpolation shows better performance in the view point of computation speed and accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.173-175
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• 2011

In this paper, we present an user interface which can show some 3D objects at various angles using tracked 3d head position and orientation. In implemented user interface, First, when user's head moves left/right (X-Axis) and up/down(Y-Axis), displayed objects are moved towards user's eyes using 3d head position. Second, when user's head rotate upon an X-Axis(pitch) or an Y-Axis(yaw), displayed objects are rotated by the same value as user's. The results of experiment from a variety of user's position and orientation show good accuracy and reactivity for 3d visualization.

• Smart Media Journal
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• v.12 no.11
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• pp.48-56
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• 2023

The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.79.4-79
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• 2002

Our goal is the development of a system that estimates the location of interested point in a room with accuracy and low cost. Non-contacting position estimating method is widely used in various areas. Among these methods, acoustic signal-based method is the cheapest and provides reasonably accurate estimation as a result of many research efforts. Most of the acoustic-signal-based three-dimensional location estimators such as 3D sonic digitizer are using the ultrasound, and are organized with two procedures; time-of-flight (TOF) estimation and localization estimation. Since the errors in estimating the TOF could be accumulated with that of localization estimate, accuracy of TOF estimate is as...

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.26-34
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• 1999

This study shows an alternative method for the determination of object's position, based on a computer vision method. This approach develops the vision system model to define the reciprocal relationship between the 3-D real space and 2-D image plane. The developed model involves the bilinear six-view parameters, which is estimated using the relationship between the camera space location and real coordinates of known position. Based on estimated parameters in independent cameras, the position of unknown object is accomplished using a sequential estimation scheme that permits data of unknown points in each of the 2-D image plane of cameras. This vision control methods the robust and reliable, which overcomes the difficulties of the conventional research such as precise calibration of the vision sensor, exact kinematic modeling of the robot, and correct knowledge of the relative positions and orientation of the robot and CCD camera. Finally, the developed vision control method is tested experimentally by performing determination of object position in the space using computer vision system. These results show the presented method is precise and compatible.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1745-1753
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• 2004

A method of estimating the pose of a three-dimensional object from a set of two-dimensioal images based on parametric eigenspace method is proposed. A Gaussian blurred edge image is used as an input image instead of the original image itself as has been used previously. The set of input images is compressed using K-L transformation. By comparing the estimation errors for the original, blurred original, edge, and blurred edge images, we show that blurring with the Gaussian function and the use of edge images enhance the data compression ratio and decrease the resulting from smoothing the trajectory in the parametric eigenspace, thereby allowing better pose estimation to be achieved than that obtainable using the original images as it is. The proposed method is shown to have improved efficiency, especially in cases with occlusion, position shift, and illumination variation. The results of the pose angle estimation show that the blurred edge image has the mean absolute errors of the pose angle in the measure of 4.09 degrees less for occlusion and 3.827 degrees less for position shift than that of the original image.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.7-12
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• 2012

In this paper, we present an effective viewpoint estimation algorithm for the Moving parallax barrier method of 3D display mobile device. Moving parallax barrier is designed to overcome the biggest problem, the limited view angle. To accomplish it, the position of the viewer's eyes or face should be estimated with strong stability and no latency. We focus on these requirements in the poor performance of mobile processors. We used a pre-processing algorithm in order to overcome the various illumination changes. And, we combined the conventional Viola-Jones face detection method and Optical-flow algorithm for robust and stable viewpoint estimation. Various computer simulations prove the effectiveness of the proposed method.

• Spatial Information Research
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• v.12 no.2
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• pp.137-149
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• 2004

For 3D modelling artificial objects with computers, we have to draw frames and paint the facet images on each side. In this paper, a geographic modelling system building automatically 3D geographic spaces using GIS data and real images of buildings is proposed. First, the 3D model of terrain is constructed by using TIN and DEM algorithms. The images of buildings are acquired with a camera and its position is estimated using vertical lines of the image and the GIS data. The height of the building is computed with the image and the position of the camera, which used for making up the frames of buildings. The 3D model of the building is obtained by detecting the facet iamges of the building and texture mapping them on the 3D frame. The proposed geographical modeling system is applied to real area and shows its effectiveness.