• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.6
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• pp.593-599
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• 2011

With increasing user applicability of geospatial data, user demand for manifold and accurate information has increased. The usefulness of these services derives from their combination of the advantages of as-built geospatial data in making new content. There is a spatial inconsistency and shape disagreement in fusing heterogeneous data. Conflation, defined as the combining of information from diverse sources so as to reconcile spatial inconsistencies and shape disagreement, is possible solution to the problem. In this research, we developed the technique for removing shape disagreement between aerial image and road map removed spatial inconsistency in advanced research. The process includes four processes: producing of a road candidate image, extraction of vertices, and generation of a graph by connecting the vertices. We could remove the shape disagreement using the extracted road that was derived from finding the road possible path.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.6
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• pp.748-757
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• 1999

본 논문에서는 차량 영역의 추출을 이용한 효율적인 교통 혼잡도 측정 시스템을 설계하고 구현한다. 차량 영역 정보의 추출은 첫째 영역 분할, 둘째 작은 영역의 제거와 영역의 직사각형화, 셋째 영역의 병합 및 삭제의 단계로 나눌 수 있다. 영역 분할 단계에서는 획득한 도로 영상을 영역 기반 영역 분할에 의해 영역으로 분할한다. 그 다음 영역 분할 후의 영역 정보 중 차량 영역을 추출하는데 영향을 미치지 않는 작은 영역들을 제거하고, 남은 영역들을 직사각형화한다. 마지막으로 차선 별로 남은 영역들을 병합, 삭제함으로써 각 차선마다 차량 영역 정보를 추출할 수 있다. 이러한 방법은 배경 영상과 같은 부가적인 정보를 사용하지 않고 도로 자체 영상만으로 교통 혼잡도를 측정할 수 있으며, 그림자의 영향이 없을 경우 적용할 수 있는 기법이다.Abstract In this paper, we designed and implemented an efficient road congestion analysis system using regional information. To extract vehicle regions from a road image, the system process the image in five steps: segmentation, small region elimination, region rectangularization, region merging and region deletion. First, we segment road image by a threshold value. Then, we eliminate useless small regions to extract vehicle region, and perform region rectangularization. Finally, we extract vehicle region of each lane of the road by region merging and deletion. This method has the advantage of measuring road congestion without additional information such as background images. But this method must be applied to road images without shadow.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.69-72
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• 2000

This study proposes a new calculation method for generating real nighttime lamp-lit images. In order to improve the color appearance in the prediction of a nighttime lamp-lighted scene, the lamp-lit image is synthesized based on spectral distribution using the estimated local spectral distribution of the headlamps and the surface reflectance of every object. The principal component analysis method is introduced to estimate the surface color of an object, and the local spectral distribution of the headlamps is calculated based on the illuminance data and spectral distribution of the illuminating headlamps. HID and halogen lamps are utilized to create beam patterns and captured road scenes are used as background images to simulate actual headlamp-lit images on a monitor. As a result, the reproduced images presented a color appearance that was very close to a real nighttime road image illuminated by single and multiple headlamps.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.511-513
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• 2004

Camera Calibration should certain)y be achieved to take an accurate measurement using image system. Calibration is to prove the relation between an measurement object and camera and to estimate twelve internal and external parameters. In this paper, we suggest that an algorithm should estimate the external parameters from the road image and use a vanishing point's character from parallel straight lines in a space. also, we use Hough Transform to estimate an accurate vanishing point. Hough Transform has one of the advantages which is an application for each road environment. we assume a variety of environments to prove the usability of a suggested algorithm and show simulation results with a computer.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.137-141
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• 2018

Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.4
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• pp.219-227
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• 2014

Natural disasters such as a ground collapse and a landslide have broken out due to the climate change of the Korea and the reckless expansion of cities and roads. The climate changes and the reckless urbanization have made the ground weak. Thus, it is important to keep a close eye on the highly weakened landslide and to prevent its natural disasters. In order to prevent these disasters, this paper presents a system of recognizing the road slide condition by measuring the displacements using laser scanner instrument. The previous system of monitoring the road slide has some problems as inaccurate recognition due to using only images from a camera, or expensive system such as artificial satellites and aircraft systems. To solve this problem, our proposed system uses the 3D range data from the laser scanner for measuring the accurate displacement of the road slide and optical flows from the Lucas-Kanade algorithm for recognizing the road slide in the image.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2843-2845
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• 2000

A lane detection based on a road model or feature all need correct acquirement of information on the lane in a image, It is inefficient to implement a lane detection algorithm through the full range of a image when being applied to a real road in real time because of the calculating time. This paper defines two searching range of detecting lane in a road, First is searching mode that is searching the lane without any prior information of a road, Second is recognition mode, which is able to reduce the size and change the position of a searching range by predicting the position of a lane through the acquired information in a previous frame. It is allow to extract accurately and efficiently the edge candidates points of a lane as not conducting an unnecessary searching. By means of removing the perspective effect of the edge candidate points which are acquired by using the inverse perspective transformation, we transform the edge candidate information in the Image Coordinate System(ICS) into the plane-view image in the World Coordinate System(WCS). We define linear approximation filter and remove the fault edge candidate points by using it This paper aims to approximate more correctly the lane of an actual road by applying the least-mean square method with the fault-removed edge information for curve fitting.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.31-37
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• 2012

Because of road construction and expansion, Update of the road traffic facilities DB is steadily increased each year, and, Increasing drivers and cars, safety signs for traffic safety are required management and additional installation continuously. To update Safety Sign database promptly, we have developed auto recognition function of safety sign, and analyzed coordinates accuracy. The purpose of this study was to propose methods to update about road traffic facilities efficiently. For this purpose, omni-directional camera was calibrated for acquisition of 3-dimensional coordinates, integrated GPS/IMU/DMI system and applied image processing. In this experiment, we proposed a effective method to update database of road traffic facilities for digital map.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.47-54
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• 2022

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.