• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.230-237
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• 2023

Self-driving is a key technology of the fourth industry and can be applied to various places such as cars, drones, cars, and robots. Among them, localiztion is one of the key technologies for implementing autonomous driving as a technology that identifies the location of objects or users using GPS, sensors, and maps. Locilization can be made using GPS or LIDAR, but it is very expensive and heavy equipment must be mounted, and precise location estimation is difficult for places with radio interference such as underground or tunnels. In this paper, to compensate for this, we proposes an image retrieval using attention module and image segmentation maps using color images acquired with low-cost vision cameras as an input.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.59-64
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• 2001

In this paper it was proposed method that solve problems of method to segment region of zero disparity and algorithm that extract binocular disparity to estimate position of object by vergence movement of moving stereo cameras experimented to compare those. There was not change of density value almost in region that change of critcal value was not found almost in image, because a high critical value was set so that critical value may be kipt changelessly about all small regions in studied treatise so far. The corresponding points were extracted wrongly by the result. By because the characteristics of small region was evaluated by autocorrelation and the critical value was established that may be proportional to the autocorrelation value, it was confirmed that corresponding points are not extracted almost by mistake and binocular disparity could by extracted with high speed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1207-1210
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• 2022

2D 이미지로부터 카메라의 위치 정보를 추정할 수 있는 Structure-from-Motion (SfM) 기술과 dense depth map 을 추정하는 Multi-view Stereo (MVS) 기술을 이용하여 2D 이미지에서 point cloud 와 같은 3D data 를 얻을 수 있다. 3D data 는 VR, AR, 메타버스와 같은 컨텐츠에 사용되기 위한 핵심 요소이다. Point cloud 는 보통 VR, AR, 메타버스와 같은 많은 분야에 이용되기 위해 mesh 형태로 변환된 후 texture 를 입히는 Texturing 과정이 필요하다. 기존의 Texturing 방법에서는 mesh의 face에 사용될 image의 outlier를 제거하기 위해 color 정보만을 이용했다. Color 정보를 이용하는 방법은 mesh 의 face 에 대응되는 image 의 수가 충분히 많고 움직이는 물체에 대한 outlier 에는 효과적이지만 image 의 수가 부족한 경우와 부정확한 카메라 파라미터에 대한 outlier 에는 부족한 성능을 보인다. 본 논문에서는 Texturing 과정의 view selection 에서 depth 정보를 추가로 이용하여 기존 방법의 단점을 보완할 수 있는 방법을 제안한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.211-221
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• 2012

To utilize omni-directional images acquired by a rotating line camera for indoor spatial information services, we should register precisely the images with respect to an indoor coordinate system. In this study, we thus develop a georeferencing method to estimate the exterior orientation parameters of an omni-directional image - the position and attitude of the camera at the acquisition time. First, we derive the collinearity equations for the omni-directional image by geometrically modeling the rotating line camera. We then estimate the exterior orientation parameters using the collinearity equations with indoor control points. The experimental results from the application to real data indicate that the exterior orientation parameters is estimated with the precision of 1.4 mm and $0.05^{\circ}$ for the position and attitude, respectively. The residuals are within 3 and 10 pixels in horizontal and vertical directions, respectively. Particularly, the residuals in the vertical direction retain systematic errors mainly due to the lens distortion, which should be eliminated through a camera calibration process. Using omni-directional images georeferenced precisely with the proposed method, we can generate high resolution indoor 3D models and sophisticated augmented reality services based on the models.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.318-324
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• 2017

We introduced a stereo camera on the aircraft to detect flight objects and to estimate the 3D position of them. The Saliency map algorithm based on PCT was proposed to detect a small object between clouds, and then we processed a stereo matching algorithm to find out the disparity between the left and right camera. In order to extract accurate disparity, cost aggregation region was used as a variable region to adapt to detection object. In this paper, we use the detection result as the cost aggregation region. In order to extract more precise disparity, sub-pixel interpolation is used to extract float type-disparity at sub-pixel level. We also proposed a method to estimate the spatial position of an object by using camera parameters. It is expected that it can be applied to image - based object detection and collision avoidance system of autonomous aircraft in the future.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.21-29
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• 2003

It is very important to reduce the computational time in estimating the parameters of vision control algorithm for robot's position control in real time. Unfortunately, the batch estimation commonly used requires too murk computational time because it is iteration method. So, the batch estimation has difficulty for robot's position control in real time. On the other hand, the Extended Kalman Filtering(EKF) has many advantages to calculate the parameters of vision system in that it is a simple and efficient recursive procedures. Thus, this study is to develop the EKF algorithm for the robot's vision control in real time. The vision system model used in this study involves six parameters to account for the inner(orientation, focal length etc) and outer (the relative location between robot and camera) parameters of camera. Then, EKF has been first applied to estimate these parameters, and then with these estimated parameters, also to estimate the robot's joint angles used for robot's operation. finally, the practicality of vision control scheme based on the EKF has been experimentally verified by performing the robot's position control.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.2
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• pp.237-242
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• 2009

Comparing with fixed-type Robots, Mobile Robots have the advantage of extending their workspaces. But this advantage need some sensors to detect mobile robot's position and find their goal point. This article describe the navigation teaching method of mobile robot using omni-directional position detection system. This system offers the brief position data to a processor with simple devices. In other words, when user points a goal point, this system revise the error by comparing its heading angle and position with the goal. For these processes, this system use a conic mirror and a single camera. As a result, this system reduce the image processing time to search the target for mobile robot navigation ordered by user.

• Journal of Korea Multimedia Society
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• v.10 no.3
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• pp.303-315
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• 2007

This paper proposes a 3D structure recovery and registration method that uses four or more common points. For each frame of a given video, a partial structure is recovered using tracked points. The 3D coordinates, camera positions and camera directions are computed at once by our improved scaled orthographic factorization method. The partially recovered point sets are parts of a whole model. A registration of point sets makes the complete shape. The recovered subsets are integrated by transforming each coordinate system of the local point subset into a common basis coordinate system. The process of shape recovery and integration is performed uniformly and linearly without any nonlinear iterative process and without loss of accuracy. The execution time for the integration is significantly reduced relative to the conventional ICP method. Due to the fast recovery and registration framework, our shape recovery scheme is applicable to various interactive video applications. The processing time per frame is under 0.01 seconds in most cases and the integration error is under 0.1mm on average.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.13-24
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• 2011

This paper deals with a 3D-pose (orientation and position) estimation problem of a circular object in 3D-space. Circular features can be found with many objects in real world, and provide crucial cues in vision-based object recognition and location. In general, as a circular feature in 3D space is perspectively projected when imaged by a camera, it is difficult to recover fully three-dimensional orientation and position parameters from the projected curve information. This paper therefore proposes a 3D pose estimation method of a circular feature using a coplanar point. We first interpret a circular feature with a coplanar point in both the projective space and 3D space. A procedure for estimating 3D orientation/position parameters is then described. The proposed method is verified by a numerical example, and evaluated by a series of experiments for analyzing accuracy and sensitivity.

• 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.