• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.121-124
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• 2000

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing Information from these detectors. The second is hybrid step, we Identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.221-224
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• 2000

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing information from these detectors. The second is global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.284-293
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• 2015

The objective of this paper is to determine the conditions of road surface by utilizing the images collected from closed-circuit television (CCTV) cameras installed on roadside. First, a technique was examined to detect wet surfaces at nighttime. From the literature reviews, it was revealed that image processing using polarization is one of the preferred options. However, it is hard to use the polarization characteristics of road surface images at nighttime because of irregular or no light situations. In this study, we proposes a new discriminant for detecting wet and dry road surfaces using CCTV image data at night. To detect the road surface conditions with night vision, we applied the wavelet packet transform for analyzing road surface textures. Additionally, to apply the luminance feature of night CCTV images, we set the intensity histogram based on HSI(Hue Saturation Intensity) color model. With a set of 200 images taken from the field, we constructed a detection criteria hyperplane with SVM (Support Vector Machine). We conducted field tests to verify the detection ability of the wet road surfaces and obtained reliable results. The outcome of this study is also expected to be used for monitoring road surfaces to improve safety.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.797-807
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• 2008

This paper proposes a new algorithm of extracting forward vehicle areas using the acquired lanes and road area information on road images with complex background to improve the efficiency of the vehicle detection. In the first stage, lanes are detected by taking into account the connectivity among the edges which are determined from a method of chain code. Once the lanes proceeding to the same direction with the running vehicle are detected, neighborhood roadways are found from the width and vanishing point of the acquired roadway of the running vehicle. And finally, vehicle areas, where forward vehicles are located on the road area including the center and neighborhood roadways, are extracted. Therefore, the proposed scheme of extracting forward vehicle area improves the rate of vehicle detection on the road images with complex background, and is highly efficient because of detecting vehicles within the confines of the acquired vehicle area. The superiority of the proposed algorithm is verified from experiments of the vehicle detection on road images with complex background.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.163-166
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• 2000

Real-time traffic detection scheme based on Computer Vision is capable of efficient traffic control using automatically computed traffic information and obstacle detection in moving automobiles. Traffic information is extracted by segmenting vehicle region from road images, in traffic detection system. In this paper, we propose the advanced segmentation of vehicle from road images using multiple local region information. Because multiple local region overlapped in the same lane is processed sequentially from small, the traffic detection error can be corrected.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.761-764
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• 2000

This paper presents a Fuzzy Neural Network (FNN) system to decide whether or not the right information of lanes can be extracted from gray-level images of road scene. The decision of noisy level of input images has been required because much noises usually deteriorates the performance of feature detection based on image processing and lead to erroneous results. As input parameters to FNN, eight noisiness indexes are constructed from a cumulative distribution function (CDF) and proved the indexes being classifiers of images as the good and the bad corrupted by sources of noise by correlation analysis between input images and the indexes. Considering real-time processing and discrimination efficiency, the proposed FNN is structured by eight input parameters, three fuzzy variables and single output. We conduct much experiments and show that our system has comparable performance in terms of false-positive rates.

• Journal of Environmental Science International
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• v.14 no.10
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• pp.905-910
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• 2005

The purpose of this study is the comparative analysis of Susan citizens' images of Eulsook-do as a coastal tourism destination before and after the construction of a road bridge across the Nakdong estuary in order to analyze local people's changes in leisure patterns. Analysis of the images of a pre-construction Eulsook-do that people aged both 40 and less and 50 and more had on five dimensions showed values higher than zero(0) that suggests neutral image, while their images of a post-construction Eulsook-do showed the shrinking size of pentagon on all five dimensions: ET(Entertainment), CA(Culture & Art), EE(Environment & Ecology), RC(Recreation) and LP(Leports) dimensions. Its pre- and post- construction image analysis conducted 20 years after it came to be built finds that the road bridge construction has led to the ecological, environmental disruption of the coast and the lower Nakdong river, having negative influence on the images of Eulsook-so.

• KIEAE Journal
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• v.3 no.2
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• pp.33-36
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• 2003

The influence of the visual information on the sound perception would be considerable. Furthermore, if the sound perception ranges in noisiness or annoyance beyond the loudness, it will depend much more on the shape of the visual information. This paper aims to estimate the influence of the several kinds of visual information on the perception of road traffic noise by means of the psycho-acoustic test method. The findings of present study on the influence of visual information on subjective noise perception are summarized as follows: Presenting visual images of mild and comfortable scenery reduced the noise perception reaction at the less noisy environments not exceeding 65 dB(A). At highly noisy environments exceeding 65 dB(A), however, the noise perception can be reduced by strong image of waterfall. Even eliminating the road traffic image may be helpful. Visual image of waterfall reduced the noise perception at all levels. It is inferred that the road traffic noise perception can be effectively ameliorated by presenting strong and real landscape images at any noisy environment.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.2
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• pp.19-25
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• 2013

In this paper, we propose an approach to tracking road regions from video sequences. The proposed method segments and tracks road regions by utilizing the prior information from the result of the previous frame. For the efficiency of the system, we have a simple assumption that the road region is usually shown in the lower part of input images so that lower 60% of input images is set to the region of interest(ROI). After initial segmentation using flood-fill algorithm, we merge neighboring regions based on color similarity measure. The previous segmentation result, in which seed points for the successive frame are extracted, is used as prior information to segment the current frame. The similarity between the road region of the previous frame and that of the current frame is measured by the modified Jaccard coefficient. According to the similarity we refine and track the detected road regions. The experimental results reveal that the proposed method is effective to segment and track road regions in noisy and non-noisy environments.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.245-245
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• 2005

This paper addresses the use of directional textural information for connecting road segments. In urban scene, some roads are occluded by buildings, casting shadow of buildings, trees, and cars on streets. Automatic extraction of road network from remotely sensed high resolution imagery is generally hindered by them. The results of automatic road network extraction will be incomplete. To overcome this problem, several perceptual grouping algorithms are often used based on similarity, proximity, continuation, and symmetry. Roads have directions and are connected to adjacent roads with certain angles. The directional information is used to guide road fragments connection based on roads directional inertia or characteristics of road junctions. In the primitive stage, roads are extracted with textural and direction information automatically with certain length of linearity. The primitive road fragments are connected based on the directional information to improve the road network. Experimental results show some contribution of this approach for completing road network, specifically in urban area.