• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.723-727
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• 2008

2D face analysis has some limitations which are pose and illumination sensitive. For these reasons, even if many researchers try to study in the 3D face analysis and processing, because of the low computing performance and the absence of a high-speed 3D scanner then a lot of research is not being able to proceed. But, due to improving of the computing performance in these days, the advanced 3D face research was now underway. In this paper, we propose the method of building a 3D face model which deal successfully with dense correspondence problem.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.177-186
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• 2015

For data fusion of laser range finder (LRF) and vision camera, accurate calibration of external parameters which describe relative pose between two sensors is necessary. This paper proposes a new calibration method which can acquires more accurate external parameters between a LRF and a vision camera compared to other existing methods. The main motivation of the proposed method is that any corner data of a known 3D structure which is acquired by the LRF should be projected on a straight line in the camera image. To satisfy such constraint, we propose a 3D geometric model and a numerical solution to minimize the energy function of the model. In addition, we describe the implementation steps of the data acquisition of LRF and camera images which are necessary in accurate calibration results. In the experiment results, it is shown that the performance of the proposed method are better in terms of accuracy compared to other conventional methods.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.765-774
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• 2019

In this paper, we propose a point cloud matching algorithm for multiple RGB-D cameras. In general, computer vision is concerned with the problem of precisely estimating camera position. Existing 3D model generation methods require a large number of cameras or expensive 3D cameras. In addition, the conventional method of obtaining the camera external parameters through the two-dimensional image has a large estimation error. In this paper, we propose a method to obtain coordinate transformation parameters with an error within a valid range by using depth image and function optimization method to generate omni-directional three-dimensional model using 8 low-cost RGB-D cameras.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• The Journal of Korea Robotics Society
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• v.11 no.4
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• pp.300-309
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• 2016

This paper proposes a method for motion estimation of consecutive cameras using 3-D straight lines. The motion estimation algorithm uses two non-parallel 3-D line correspondences to quickly establish an initial guess for the relative pose of adjacent frames, which requires less correspondences than that of current approaches requiring three correspondences when using 3-D points or 3-D planes. The estimated motion is further refined by a nonlinear optimization technique with inlier correspondences for higher accuracy. Since there is no dominant line representation in 3-D space, we simulate two line representations, which can be thought as mainly adopted methods in the field, and verify one as the best choice from the simulation results. We also propose a simple but effective 3-D line fitting algorithm considering the fact that the variance arises in the projective directions thus can be reduced to 2-D fitting problem. We provide experimental results of the proposed motion estimation system comparing with state-of-the-art algorithms using an open benchmark dataset.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.7-14
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• 2006

본 논문에서는 3D 복원과 카메라 측정과정 없이 정확하게 카메라 자세를 계산하고 가상객체를 비디오에 합성하기 위한 단일 프레임 기반의 고속 계산 기법을 제안한다. 객체의 로컬 좌표와 단일 이미지에서의 대응되는 이미지 좌표로부터 카메라 자세를 계산한다. 정사영 투영모델에서의 분해기법에 기반한 구조 계산 방법으로 카메라 자세의 고속 추정이 가능하다. 정사영 투영모델에 기반하기 때문에 참조점의 설정에 따라 정확도가 달라진다. 객체에 따라 참조점을 설정하여 정확한 카메라 자세를 계산하는 방법을 제안한다. 카메라 자세 및 물체의 형태는 단일 프레임 기반으로 수행되며 카메라 자세 추정 결과가 즉시 비디오 합성에 사용될 수 있도록 하였다. 제안하는 기법의 유효성 입증을 위해 실사 비디오에 기반한 증강현실시스템을 구현하고 카메라 자세 계산과 비디오 합성의 전체 과정을 단일 프레임에 기반하여 실험을 수행하고 제안 기법의 실용성을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.712-715
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• 1997

In image jacobian based visual servoing, generally, inverse jacobian should be calculated by complicated coordinate transformations. These are required excessive computation and the singularity of the image jacobian should be considered. This paper presents a visual servoing to control the pose of the robotic manipulator for tracking and grasping 3-D moving object whose pose and motion parameters are unknown. Because the object is in motion tracking and grasping must be done on-line and the controller must have continuous learning ability. In order to estimate parameters of a moving object we use the kalman filter. And for tracking and grasping a moving object we use a fuzzy inference based reinforcement learning algorithm of dynamic recurrent neural networks. Computer simulation results are presented to demonstrate the performance of this visual servoing

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1153-1163
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• 2014

An efficient 3D SLAM (Simultaneous Localization and Map Building) method is developed for urban building environments using a tilted 2D LRF (Laser Range Finder), in which a 3D map is composed of perpendicular/horizontal planar polygons. While the mobile robot is moving, from the LRF scan distance data in each scan period, line segments on the scan plane are successively extracted. We propose an "expected line segment" concept for matching: to add each of these scan line segments to the most suitable line segment group for each perpendicular/horizontal planar polygon in the 3D map. After performing 2D localization to determine the pose of the mobile robot, we construct updated perpendicular/horizontal infinite planes and then determine their boundaries to obtain the perpendicular/horizontal planar polygons which constitute our 3D map. Finally, the proposed SLAM algorithm is validated via extensive simulations and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.762-766
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• 2003

In this paper, we present a new approach to solve the problem of estimating the camera 3-D location and orientation from a matched set of 3-D model and 2-D image features. An iterative least-square method is used to solve both rotation and translation simultaneously. Because conventional methods that solved for rotation first and then translation do not provide good solutions, we derive an error equation using roll-pitch-yaw angle to present the rotation matrix. To minimize the error equation, Levenberg-Marquardt algorithm is introduced with uniform sampling strategy of rotation space to avoid stuck in local minimum. Experimental results using real images are presented.