• Journal of the Korean Institute of Landscape Architecture
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• v.24 no.2
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• pp.1-12
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• 1996

In order to develope a road landscape that is in harmony with landscaping purpose, degree of sight occupation by slopes at road sides was measured and physical elements composing the slope scenery were visually evaluated and the results are as follows : In analysis of sight occupation ratio by perspective method, gradient of the slopes influenced more on the sight occupation than height did and the driving lane occupied 2 to 3% more proportion of sight than the passing lane. When there is slope at one side of the road, difference in sight occupation between the lanes was increasing with deceased height and with increased gradient of the slopes. In visual analysis of the slope scenery, negative image was increasing with narrow road, increased height and gradient of the slopes. In visual analysis of the slope scenery, negative image was increasing with narrow road, increased height and gradient of the slopes. Particularly, the effect of gradient was critical on scenery. The effect of the slopes was negative at 60$^{\circ}$ or more but positive at 45$^{\circ}$or less gradient. This phenomenon was more conspicuous with wide 4 lane roads than wide 2 lane roads. Although direct comparison is difficult due to a great difference between Korea and U.S.A. in climate, land condition, road dimension, and public process of purchasing land, etc, it is desirable to treat road sides so that the scenery is in harmony with landscape around as well as emphasizing the regional characteristics, also giving friendly and comfortable image to drivers and nearby residents in addition to safety as can be seen in U.S.A.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.34-39
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• 2002

Road information is very important for topographic mapping, transportation application, urban planning and other related application fields. Therefore, automatic detection of road networks from spatial imagery, such as aerial photos and satellite imagery can play a central role in road information acquisition. In this paper, we use least squares correlation matching alone for road center tracking and show that it works. We assumed that (bright) road centerlines would be visible in the image. We further assumed that within a same road segment, there would be only small differences in brightness values. This algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation at the target window. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. A 1m resolution IKONOS images over Seoul and Daejeon were used for tests. The results showed that this algorithm could extract road centerlines in any orientation and help in fast and exact he ad-up digitization/vectorization of cartographic images.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.409-422
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• 2022

The purpose of this paper is to explore how the country-of-origin image mediates the effect of national stereotypes along two dimensions of perceived competence and warmth, on consumers' consumption behaviors, especially in today's environment, the capricious COVID-19 and the deepening and expanding "The Belt and Road" initiative. Research design, data, and methodology: After collecting 1500 primary data from twelve countries along the 21^st - Century Maritime Silk Road, this paper conducts ANOVA and SEM in SPSS25.0 and AMOS 24.0 separately to analyze measurements, structural models, and hypotheses via using 1277 final samples. The mediation results illustrate the asymmetric dominance of the two dimensions of national stereotypes, indicating that the country-of-origin image shows the complementary mediation in the effect of perceived competence on purchase intention; whereas, the country-of-origin image holds the indirect-only mediation in the impact of perceived warmth on purchase intention. The results of the moderation show that the effect of country-of-origin image on purchase intention is more significant for consumers who perceive COVID-19 in China to be of lesser severity than those who believe it to be of higher severity. Based on the paper's results, some implications for practice and theory are highlighted.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.871-879
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• 1997

The comversion of printed maps into computerixed data bases is an enormous rask. Thus the autmaotion of the conversion process is essential. Efficient computer representation of printed maps and line drawings depends on codes assigened to chracaters, symbools, and vestor representation of the graphics. In many cases, maps ard constructed in a number of layers, where each layer is printed in a distinct color, and it represents a subste of the map infromation. In order to properly repressnet road information from color map images, an automatic road extraction algorithm is proposed. Road image is separated from graghics by color segmentation, and then restored by the proposed concurrent conditional dilation operation. The internal and external noise of the road image is eliminated by opening and closing operation. By thining and vectorizing line segments, the desited road information is extracted.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.63-69
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• 2011

Recent growth of the geospatial information on the web made it possible to applicate spatial data. Also, the demand for rich and latest information shows a steady growth. The need for the new service using conflation of the existing spatial databases is on the increase. The information delivery of the services using the road vector and aerial image is reached intuitionally and accurately. However, the spatial inconsistencies in map services such as Daum map, Naver map and Google map is the problem. Our approach is processed to extract the road candidate image, match the template and filter the control points pair using vector median. Finally, CNS node and link are aligned to the real road with the aerial image. The experimental results show that our approach can align a set of CNS node and link with aerial imagery for daejon, such that the completeness and correctness of the aligned road have improved about 35% compare with the original roads.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.759-764
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• 1998

We propose a novel algorithm capable of recognizing the road lane by image processing. Considering the fact that the direction and location of road lane are maintained similarly in successive images we formulate a function to represent the property. However, as noises play the role of making a lot of similar patterns appear and disappear in the road image, keeping of robustness in the lane detection has been known a difficult work. To overcome this problem, we introduce the following three ideas: 1) design of a function based on an edge direction and magnitude, 2) construction of a recursive filter to estimate the function recursively for successive images, 3) principal axis-based line fitting. These concepts enhance the adaptability to cope with the random environment of traffic scene and eventually lead to the reliable detection of a road lane.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.301-303
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• 2003

The extraction of 3D road network from high-resolution aerial images is still one of the current challenges in digital photogrammetry and computer vision. For many years, there are many researcher groups working for this task, but unt il now, there are no papers for doing this with TLS (Three linear scanner), which has been developed for the past several years, and has very high-resolution (about 3 cm in ground resolution). In this paper, we present a methodology of road extraction from high-resolution digital imagery taken over urban areas using this modern photogrammetry’s scanner (TLS). The key features of the approach are: (1) Because of high resolution of TLS image, our extraction method is especially designed for constructing 3D road map for next -generation digital navigation map; (2) for extracting road, we use the global context of the intensity variations associated with different features of road (i.e. zebra line and center line), prior to any local edge. So extraction can become comparatively easy, because we can use different special edge detector according different features. The results achieved with our approach show that it is possible and economic to extract 3D road data from Three Linear Scanner to construct next -generation digital navigation road map.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.463-466
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• 2007

In this study, 3D building reconstruction and road design were performed using LiDAR data, digital map and airborne digital image. Information for tourism was extracted from digital maps (scale: 1/5,000) of Jeju Island, and then route of the road was determined for road design. Reconstructed buildings, aerial image and designed road were overlayed with tourism information for 3D simulation. In addition, landscape analysis was performed and result of the road design was visualized.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.465-472
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• 2004

Analog CPPN-based optimal road boundary detection algorithm for autonomous vehicle is proposed. The CPPN is a massively connected analog parallel array processor. In the paper, the dynamic programming which is an efficient algorithm to find the optimal path is implemented with the CPPN algorithm. If the image of road-boundary information is utilized as an inter-cell distance, and goals and start lines are positioned at the top and the bottom of the image, respectively, the optimal path finding algorithm can be exploited for optimal road boundary detection. By virtue of the parallel and analog processing of the CPPN and the optimal solution of the dynamic programming, the proposed road boundary detection algorithm is expected to have very high speed and robust processing if it is implemented into circuits. The proposed road boundary algorithm is described and simulation results are reported.