• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.396-399
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• 2009

Stereo vision system is useful method for inferring 3D depth information from two or more images. So it has been the focus of attention in this field for a long time. Stereo matching is the process of finding correspondence points in two or more images. A central problem in a stereo matching is that it is difficult to satisfy both the computation time problem and accuracy at the same time. To resolve this problem, we proposed a new stereo matching technique using weighted cost aggregation. To begin with, we extract the weight in given stereo images based on features. We compute the costs of the pixels in a given window using correlation of weighted color, brightness and distance information. Then, we match pixels in a given window between the reference and target images of a stereo pair. To demonstrate the effectiveness of the algorithm, we provide experimental data from several synthetic and real scenes. The experimental results show the improved accuracy of the proposed method.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.298-304
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• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.643-644
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• 2012

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.279-286
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• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• The Magazine of the IEIE
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• v.21 no.10
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• pp.36-46
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• 1994

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.44-56
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• 2007

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.3
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• pp.21-29
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• 2011

This paper presents a method that estimates distance and velocity of an object with reliability regardless of maneuver status of the target in stereo vision system. A stereo vision system can calculate a distance with disparity from left and right images. However, the distance estimation error may occur due to quantization error of image pixel. A sub-pixel interpolation method minimizes the quantization error and estimates accurate disparity with real value. Extended Kalman filter (EKF) was used to minimize the error covariance and estimate the object's velocity. However, divergence problem occurs due to model uncertainty when a target maneuvers highly, which makes the estimation error increase. In this paper, we propose a virtual model extended Kalman filter (VMEKF) method that minimizes the processing time and provides reliable estimation ability regardless of maneuver status. Computer simulations and experimental results in real road environments demonstrate that the proposed method gives a reliable estimation performance and reduces processing time under various maneuver status while comparing other estimation filters.

• The Journal of the Korea Contents Association
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• v.16 no.1
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• pp.515-520
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• 2016

Volumetric image system has many applications recently in education, 3D movie, medical images but these applications have several problems that need to be overcome. Volumetric display may process a amount of visual data and design the high efficient vision system for realtime display. In case of stereo system for volumetric display motion vectors, disparity vectors from the stereoscopic sequences and residual images with the reference images has been transmitted, and the stereoscopic sequences have been reconstructed at the receiver for volumetric display. So central issue for the design of efficient volumetric image system lies in selecting an appropriate stereo matching and robust vision system. In this paper, we proposed high efficient vision system, which design vision stage with rotating and moving horizontally, and match the successive stereo image efficiently. In experimental results with volumetric image system, the proposed method represents high efficiency with minimizing error and low computational load for volumetric display.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.279-287
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• 2009

In this paper, a hierarchical stereo matching with color information is proposed. To generate an initial disparity map, feature based stereo matching is carried out and to generate a final disparity map, hierarchical stereo matching is carried out. The boundary (edge) region is obtained by segmenting a given image into R, G, B and White components. From the obtained boundary, disparity is extracted. The initial disparity map is generated when the extracted disparity is spread to the surrounding regions by evaluating autocorrelation from each color region. The initial disparity map is used as an initial value for generating the final disparity map. The final disparity map is generated from each color region by changing the size of a block and the search range. 4 test images that are provided by Middlebury stereo vision are used to evaluate the performance of the proposed algorithm objectively. The experiment results show better performance compared to the Graph-cuts and Dynamic Programming methods. In the final disparity map, about 11% of the disparities for the entire image were inaccurate. It was verified that the boundary for the non-contiguous point was clear in the disparity map.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.8
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• pp.87-95
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• 2007

In this paper, we analyzed the mr detection and the stability of the object tracking system by an adaptive stereo object hacking using region-based MAD(Mean Absolute Difference) algorithm and the modified PID(Proportional Integral Derivative)-based pan/tilt controller. That is, in the proposed system, the location coordinates of the target object in the right and left images are extracted from the sequential stereo input image by applying a region-based MAD algorithm and the configuration parameter of the stereo camera, and then these values could effectively control to pan/tilt of the stereo camera under the noisy circumstances through the modified PID controller. Accordingly, an adaptive control effect of a moving object can be analyzed through the advanced system with the proposed 3D robot vision, in which the possibility of real-time implementation of the robot vision system is also confirmed.