• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.655-670
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• 2014

In this paper, we propose an efficient and robust lane detection method for detecting immediate left and right lane boundaries of the lane in the roads. The proposed method are based on hyperbolic lane model and the reliable line segment clustering. The reliable line segment cluster is determined from the most probable cluster obtained from clustering line segments extracted by the efficient LSD algorithm. Experiments show that the proposed method works robustly against lanes with difficult environments such as ones with occlusions or with cast shadows in addition to ones with dashed lane marks, and that the proposed method performs better compared with other lane detection methods on an CMU/VASC lane dataset.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.174-179
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• 2005

This paper presents 3D line segment extraction method, which can be used in generating 3D rooftop model. The core of our method is that 3D line segment is extracted by using line fitting of elevation data on 2D line coordinates of ortho-image. In order to use elevations in line fitting, the elevations should be reliable. To measure the reliability of elevation, in this paper, we employ the concept of self-consistency. We test the effectiveness of the proposed method with a quantitative accuracy analysis using synthetic images generated from Avenches data set of Ascona aerial images. Experimental results indicate that the proposed method shows average 30 line errors of .16 - .30 meters, which are about $10\%$ of the conventional area-based method.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.141-143
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• 2004

This paper represents 3D line segment extraction method, which can be used in generating 3D rooftop model. The core of our method is that 3D line segment is extracted by using line fitting of elevation data on 2D line coordinates of ortho-image. In order to use elevations in line fitting, the elevations should be reliable. To measure the reliability of elevation, in this paper, we employed the concept of self-consistency. We carry out the experiment of 3D line extraction using synthetic images generated from Avenches data set of Ascona aerial images.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1496-1499
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• 2004

The paper persents an efficient method of extracting line segment in a grid map. The grid map is composed of 2-D grids that have both the occupancy and orientation probabilities based on the simplified Bayesian updating model. The probabilities and orientations of cells in the grid map are continuously updated while the robot explorers to their values. The line segments are, then, extracted from the clusters using Hough transform methods. The eng points of a line segment are evaluated from the cells in each cluster, which is simple and efficient comparing to existing methods. The proposed methods are illustrated by sets of experiments in an indoor environment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.4
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• pp.46-55
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• 2000

This paper presents an algorithm for 3D building reconstruction from a pair of stereo aerial images using the 3D building model and the linear segments of building. Direct extraction of linear segments from original building images using parametric building model is attempted instead of employing the conventional procedures such as edge detection, linear approximation and line linking A segment measure function is simultaneously applied to each line segment extracted in order to improve the accuracy of building detection comparing to individual linear segment detection. The algorithm has been applied to pairs of stereo aerial images and the result showed accurate detection and reconstruction of buildings.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.250-256
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• 2015

We present a novel method to model the body posture of a worm for vision-based automatic monitoring and analysis. The worm considered in this study is a Caenorhabditis elegans (C. elegans), which is popularly used for research in biological science and engineering. We model the posture by an open chain of a few curved or rigid line segments, in contrast to previously published approaches wherein a large number of small rigid elements are connected for the modeling. Each link segment is represented by only two parameters: an arc angle and an arc length for a curved segment, or an orientation angle and a link length for a straight line segment. Links in the proposed method can be readily related using the Denavit-Hartenberg convention due to similarities to the kinematics of an articulated manipulator. Our method was tested with real worm images, and accurate results were obtained.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.277-285
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• 2010

This paper describes an approach to analyze geometrical information of building images for understanding outdoor environment of autonomous navigation robot. Line segments and color information are used to classily a building with the other objects such as sky, trees, and roads. The line segments and their two neighboring regions are extracted from detected edges in image. The model of line segment (MLS) consists of color information of neighbor regions. This model rules out the line segments of non-building face. A building face converges into dominant vanishing points (DVPs) which include one vertical point and one of five horizontal points in maximum. The intersection of vertical and horizontal lines creates a facet of building. The geometrical characteristics such as the center coordinates, area, aspect ratio and aligned coexistence are used for extracting the windows in the building facet. In experiments, 150 building faces and 1607 windows were detected from the database of outdoor environment. We found that this result shows 94.46% detection rate. These experimental images were all taken in Ulsan metropolitan city in Korea under difference of viewpoints, daytime, camera system and weather condition.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2921-2923
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• 2005

This paper presents to generate 3D building model using estimation of rooftop surface after 3D line segment extraction using hybrid stereo matching techniques in terms of the co-operation of area-based stereo and feature-based stereo. we first performed a junction extraction from 3D line segment data which was obtained by stereo images, and finally generated building's reliable rooftop surface model using LSE(Least Square Error) method after creating surfaces by grouped and fixed junction points. we generated synthetic images for experimentation by photo-realistic simulation on Avenches data set of Ascona aerial images.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.

• Structural Engineering and Mechanics
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• v.71 no.3
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• pp.305-315
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• 2019

Extremely long-span transmission tower-line system is an indispensable portion of an electricity transmission system, and its failures or collapse can impact on the entire electricity grid, affect the modern life, and cause great economic losses. It is therefore imperative to investigate the failure and safety of the transmission tower subjected to ground motions. In the present study, a detailed finite element (FE) model of a representative extremely long-span transmission tower-line system is established. A segmental damage indicator (SDI) is proposed to quantitatively assess the damage level of each segment of the transmission tower under earthquakes. Additionally, parametric studies are conducted to investigate the influence of different ground motions and incident angles on the ultimate capacity and weakest segment of the transmission tower. Finally, the collapse fragility curve in terms of the maximum SDI value and PGA is plotted for the exampled transmission tower. The results show that the proposed SDI can quantitatively assess the damage level of the segments, and thus determine the ultimate capacity and weakest segment of the transmission tower. Moreover, the different ground motions and incident angles have a significant influence on the SDI values of the transmission tower, and the collapse fragility curve is utilized to evaluate the collapse resistant capacity of the transmission tower subjected to ground motions.