• Proceedings of the KSR Conference
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• 2003.05a
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• pp.321-325
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• 2003

The technology for the multi-dimensional terrain perspective view can be used as an important factors in planning and designing for the various construction projects. In this study, the stereo image perspective view has been generated for the multi-dimension analysis by combining useful digital map and remotely sensed satellite images. In the course of experimenting with the multi-dimensional topography generated by the combination of the front-projected image by the precise GCP and DEM from the contour line, the technology has been developed to offer the multi-dimensional access to the potential construction sites from the nearby main roads. This stereo image bird's eye view has made it possible to make multi-dimensional analysis on the terrain, which provides real time virtual access to the designated construction sites and will be a versatile application for development planning and construction projects.

• Spatial Information Research
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• v.15 no.3
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• pp.203-218
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• 2007

The purpose of this article is to set up the scientific and reasonable norm of location and service area determination instead of the pro-administrative lacking availability, so as to propose more practical and reasonable standard of space unit for the location of facilities. This article has accepted the method of Voronoi Diagram as a new scientific and reasonable criteria. The article chooses and realizes a model that can propose a new service area, transform and apply to improve its reality, and assesses which has more reality and compatibility by comparing the models. The result from this procedure can be adapted in objectification of the service area determination and formation of the standard space unit.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.30-38
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• 2003

This paper presents an efficient region-based motion segmentation method for segmenting of moving objects in a traffic scene with a focus on a Video Monitoring System (VMS). The presented method consists of two phases: motion detection and motion segmentation. Using the adaptive thresholding technique, the differences between two consecutive frames are analyzed to detect the movements of objects in a scene. To segment the detected regions into meaningful objects which have the similar intensity and motion information, the regions are initially segmented using a k-means clustering algorithm and then, the neighboring regions with the similar motion information are merged. Since we deal with not the whole image, but the detected regions in the segmentation phase, the computational cost is reduced dramatically. Experimental results demonstrate robustness in the occlusions among multiple moving objects and the change in environmental conditions as well.

• Journal of the Korean Institute of Telematics and Electronics
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• v.10 no.5
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• pp.16-20
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• 1973

The most important thing in locating disturbances in optimal rapid traffic singnal control is to collect information cocerning toraffit flow by means of a detection method. In order to set up an optimal traffic singnal period, the analysis of a delay time phenomena in the signal period must also be considered. In fact, each of the distributed traffic quantities on the road are not similar factors in view of speeds and distances of succeeding cars. The causing factors are analyzed by the method of control engineering analysis, and they are coincident with disturbance. Thus distubances cause errors. Distubances are fuctions of time, and are classified into three conditions: Natural road state and weather are the first. The second is structures and function of vehicles, and the third is inducedbydrivers. This thesis deals with the last two cases except the first one for maximum utilization of the existing road state and weather conditions. The first condition remains constant, and then there exist some relations between vehicles and drivers. In the long run, it can be shown that the scheme for minimizing whole errors in the optimal traffic signal time setting is definitely presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.1-11
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• 2014

In this paper, travel speed patterns were deducted based on historical DSRC travel speed data using Decision Tree technique to improve availability of the massive amount of historical data. These patterns were designed to reflect spatio-temporal vicissitudes in reality by generating pattern units classified by months, time of day, and highway sections. The study area was from Seoul TG to Ansung IC sections on Gyung-bu highway where high peak time of day frequently occurs in South Korea. Decision Tree technique was applied to categorize travel speed according to day of week. As a result, five different pattern groups were generated: (Mon)(Tue Wed Thu)(Fri)(Sat)(Sun). Statistical verification was conducted to prove the validity of patterns on nine different highway sections, and the accuracy of fitting was found to be 93%. To reduce travel pattern errors against individual travel speed data, inclusion of four additional variables were also tested. Among those variables, 'traffic condition on previous month' variable improved the pattern grouping accuracy by reducing 50% of speed variance in the decision tree model developed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.219-230
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• 2017

This paper proposes a vehicle to road tracking algorithm based on vision sensor by using EKF(Extended Kalman Filter). The lateral offset, heading angle, and curvature which are obtained from vehicle to road tracking might be used as inputs to steering controller of LKAS(Lane Keeping Assist System) or for the warning decision logic of LDWS(Lane Departure Warning System). To the end, in this paper, the yaw rate, steering angle, and vehicle speed as well as lane raw points together with considering of vehicle lateral dynamics are utilized to improve the exactness and convergence of the vehicle to road tracking. The proposed algorithm has been tested at a proving ground that consists of straight and curve sections and compared with GPS datum using DGPS-RTK equipment to show the feasibility of the proposed algorithm.

• Management & Information Systems Review
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• v.4
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• pp.257-284
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• 2000

According as the drivers increase who have their cars, the comprehensive studies on the automobile for the traffic safety have been raised as the important problems. Visual Recognition System for radio-controled driving is a part of the sensor processor of Unmanned Autonomous Vehicle System. When a driver drives his car on an unknown highway or general road, it produces a model from the successively inputted road traffic information. The suggested Recognition System of the Road Traffic Safety Information Board is to recognize and distinguish automatically a Road Traffic Safety Information Board as one of road traffic information. The whole processes of Recognition System of the Road Traffic Safety Information Board suggested in this study are as follows. We took the photographs of Road Traffic Safety Information Board with a digital camera in order to get an image and normalize bitmap image file with a size of $200{\times}200$ byte with Photo Shop 5.0. The existing True Color is made up the color data of sixteen million kinds. We changed it with 256 Color, because it has large capacity, and spend much time on calculating. We have practiced works of 30 times with erosion and dilation algorithm to remove unnecessary images. We drawing out original image with the Region Splitting Technique as a kind of segmentation. We made three kinds of grouping(Attention Information Board, Prohibit Information Board, and Introduction Information Board) by RYB( Red, Yellow, Blue) color segmentation. We minimized the image size of board, direction, and the influence of rounding. We also minimized the Influence according to position. and the brightness of light and darkness with Eigen Vector and Eigen Value. The data sampling this feature value appeared after building the learning Code Book Database. The suggested Recognition System of the Road Traffic Safety Information Board firstly distinguished three kinds of groups in the database of learning Code Book, and suggested in order to recognize after comparing and judging the board want to recognize within the same group with Nearest Neighborhood Decision Making.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.163.1-163.1
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• 2012

이 연구는 우주물체에 대한 광학감시 및 추적을 수행하기 위한 선행연구로, 궤도전파 시뮬레이터를 개발하여 궤도상 위성의 광학관측가능성을 분석하고 광학관측 여부를 판단하는 것을 목표로 한다. 연구의 주 내용은 주어진 궤도정보를 바탕으로 하는 태양동기궤도(Sun-Synchronous Orbit; SSO) 위성, Dawn-dusk 위성, 저궤도(Low Earth Orbit; LEO) 위성, 정지궤도(Geostationary Orbit; GEO) 위성 등 궤도상 위성의 추정궤도 전파와 자국위성의 광학관측가능성 분석으로 구성된다. 각각의 궤도전파 정밀도 및 광학관측가능성 분석성능을 확인하기 위해 AGI(Analytical Graphics Incorporated)사의 STK(Satellite Tool Kit) 시뮬레이션 프로그램을 사용하여 개발된 궤도전파 시뮬레이터와 비교하였다. 시뮬레이션 과정에서 광학관측의 제한조건을, 지구반영(penumbra)과 태양직사광(direct sun)에서만 관측하며, 고도(elevation angle)의 최소값은 20도, 태양고도(Sun elevation angle)의 최대값은 -10도로 설정하였다. 광학관측이 이루어지는 가상의 관측소는 임의로 선정하였으며, 기본적인 관측시간은 1년으로 잡고, 계절의 변화에 따른 광학관측가능성 궤적의 변화를 보기위해 춘하추동에 대해서 각각 3일이내의 기간 동안 시뮬레이션을 수행하였다. 결과적으로, 우주물체 광학감시 및 추적을 수행하기 위한 광학관측가능성 분석성능은 궤도전파 시뮬레이터 및 초기궤도요소 정밀도, 좌표변환과정 오차 등의 영향을 받으며, 설정된 제한조건에 따라 광학관측 지속시간의 차이가 발생한다. 연구결과를 통해 궤도상 위성의 궤도를 추정하기 위한 위성의 궤도전파 시뮬레이터를 개발하고, 자국위성의 관측가능성 분석을 통해 광학감시 및 추적시스템의 운영이 원활히 이루어질 수 있도록 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.621-628
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• 2015

This paper describes an algorithm for detecting vehicles detection in real time. The proposed algorithm has the technique based on computer vision and image processing. In real, complex environment such as one with road traffic, many algorithms have great difficulty such as low detection rate and increasing computational time due to complex backgrounds and rapid changes. To overcome this problem in this paper, the proposed algorithm consists of the following methods. First, to effectively separate the candidate regions, we use vertical and horizontal edge information, and shadow values from input image sequences. Second, we extracts features by using HOG from the selected candidate regions. Finally, this paper uses the OS fuzzy-ELM based on SLFN to classify the extracted features. The experimental results show that the proposed method perform well for detecting vehicles and improves the accuracy and the computational time of detecting.

• Journal of Korea Game Society
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• v.7 no.4
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• pp.63-70
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• 2007

Since the fisheye lens has a wide field of view, it can capture the scene and illumination from all directions from far less number of omnidirectional images. Due to these advantages of the omnidirectional camera, it is widely used in surveillance and reconstruction of 3D structure of the scene In this paper, we present a new self-calibration algorithm of omnidirectional camera from uncalibrated images by considering the inlier distribution. First, one parametric non-linear projection model of omnidirectional camera is estimated with the known rotation and translation parameters. After deriving projection model, we can compute an essential matrix of the camera with unknown motions, and then determine the camera information: rotation and translations. The standard deviations are used as a quantitative measure to select a proper inlier set. The experimental results showed that we can achieve a precise estimation of the omnidirectional camera model and extrinsic parameters including rotation and translation.