• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.9-15
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• 2015

We present a robust power transmission lines detection method based on vanishing point estimation. Vanishing point estimation can be helpful to detect power transmission lines because parallel lines converge on the vanishing point in a projected 2D image. However, it is not easy to estimate the vanishing point correctly in an image with complex background. Thus, we first propose a vanishing point estimation method on power transmission lines by using a probabilistic voting procedure based on intersection points of line segments. In images obtained by our system, power transmission lines are located in a fan-shaped area centered on this estimated vanishing point, and therefore we select the line segments that converge to the estimated vanishing point as candidate line segments for power transmission lines only in this fan-shaped area. Finally, we detect the power transmission lines from these candidate line segments. Experimental results show that the proposed method is robust to noise and efficient to detect power transmission lines.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.300-309
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• 2018

Detection of vanishing point is a challenging task in the situations where there are several structures with straight lines. Commonly used approaches for determining vanishing points involves finding the straight lines using edge detection and Hough transform methods. This approach often fails to perform effectively when there are a lot of straight lines found. The lines not meeting at a vanishing point are considered to be noises. In such situation, finding right candidate lines for detecting vanishing points is not a simple task. This paper proposes to use reference objects for vanishing point detection. By analyzing a reference object, it identifies the contour of the object, and derives a polygon from the contour information. Then the edges of the detected polygon are used to find the vanishing points. Our experimental results show that the proposed approach can detect vanishing points with comparable accuracy to the existing edge detection based method. Our approach can also be applied effectively even to complex situations, where too many lines generated by the existing methods make it difficult to select right lines for the vanishing points.

• Journal of KIISE
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• v.42 no.5
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• pp.642-651
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• 2015

A vanishing point is a point where parallel lines converge, and they become evident when a camera's lenses are used to project 3D space onto a 2D image plane. Vanishing point detection is the use of the information contained within an image to detect the vanishing point, and can be utilized to infer the relative distance between certain points in the image or for understanding the geometry of a 3D scene. Since parallel lines generally exist for the artificial structures within images, line-detection-based vanishing point-detection techniques aim to find the point where the parallel lines of artificial structures converge. To detect parallel lines in an image, we detect edge pixels through edge detection and then find the lines by using the Hough transform. However, the various textures and noise in an image can hamper the line-detection process so that not all of the lines converging toward the vanishing point are obvious. To overcome this difficulty, it is necessary to assign a different weight to each line according to the degree of possibility that the line passes through the vanishing point. While previous research studies assigned equal weight or adopted a simple weighting calculation, in this paper, we are proposing a new method of assigning weights to lines after noticing that the lines that pass through vanishing points typically belong to artificial structures. Experimental results show that our proposed method reduces the vanishing point-estimation error rate by 65% when compared to existing methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.6
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• pp.361-367
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• 2016

In this paper, we propose an improved single view metrology (SVM) algorithm to accurately measure the height of objects. In order to accurately measure the size of objects, vanishing points have to be correctly estimated. There are two methods to estimate vanishing points. First, the user has to choose some horizontal and vertical lines in real world. Then, the user finds the cross points of the lines. Second, the user can obtain the vanishing points by using software algorithm such as [6-9]. In the former method, the user has to choose the lines manually to obtain accurate vanishing points. On the other hand, the latter method uses software algorithm to automatically obtain vanishing points. In this paper, we apply image resizing and edge sharpening as a pre-processing to the algorithm in order to improve performance. The estimated vanishing points algorithm create four vanishing point candidates: two points are horizontal candidates and the other two points are vertical candidates. However, a common image has two horizontal vanishing points and one vertical vanishing point. Thus, we eliminate a vertical vanishing point candidate by analyzing the histogram of angle distribution of vanishing point candidates. Experimental results show that the proposed algorithm outperforms conventional methods, [6] and [7]. In addition, the algorithm obtains similar performance with manual method with less than 5% of the measurement error.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1758-1763
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• 1991

This paper describes a new method to determine the 3D-shape of objects consisting of specular planar surfaces. This method exploits a light source which is made of a diffuse plane with a grid pattern encoded in an M-sequence and uses a single image of the light source reflected by the objects to acquiring orientations and positions of the surfaces of the objects. When grid lines of the light source are reflected by a specular planar surface and perspectively projected on an image plane, a set of lines vanishing at a point are obtained on the image plane. The orientation of the specular planar surface is determined by using the vanishing point, and the position is determined by using the correspondence between lines on the image and lines on the light source, which is obtained by employing a characteristic regularity of the M-sequence. Before the vanishing points are calculated, the lines on the image are classified and correlated with the surfaces of objects by using slopes and positions of the lines and the regularity of the M-sequence. This method requires only a single image.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.39-46
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• 2017

In this paper, we proposes method of vanishing point detection using orthogonality of vanishing point, under the "Manhattan World" assumption that the structure of the city is mostly grid and vanishing point are orthogonal to each other. The feature that the vanishing point are orthogonal to each other can be useful for inferring the missing point that are not detected among the three vanishing point, and prevent the vanishing point detected close to the other vanishing point. In this paper, we detect Vertical vanishing point through statistical approach and detect Horizontal and Front vanishing point through structural approach. Experimental results show that the proposed method improves the detection accuracy of the vanishing point compared with the existing method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.96-101
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• 2011

Vanishing point can be defined as a point generated by converged perspective lines, which are parallel in the real world. In this paper, we propose a real-time vanishing point detection algorithm using this fundamental feature of vanishing point. The existing methods 1) require high computational cost or 2) are restricted to specific image contents. The proposed method detects the vanishing point in images based on the block-wise HOG (Histogram of Oriented Gradient) descriptor. First, we compute the HOG descriptor in a block-wise manner, then estimate the location of the vanishing point using the proposed dynamic programing. Experiments are performed on diverse images to confirm the efficiency of the proposed method.

• The Magazine of the IEIE
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• v.23 no.12
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• pp.115-125
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using one degree perspective Invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of a simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two locally parallel sloe-lines are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points. Feature points for cross ratio are extracted robustly using a vanishing point and intersection points between two locally parallel side-lines and vertical lines. Also the local position estimation problem has been treated when feature points exist less than 4points in the viewed scene. The robustness and feasibility of our algorithms have been demonstrated through real world experiments In Indoor environments using an indoor mobile robot, KASIRI-II(KAist Simple Roving Intelligence).

• Bulletin of the Korean Mathematical Society
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• v.49 no.4
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• pp.685-692
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• 2012

Consider a hypersurface $M^n$ in $\mathbb{R}^{n+1}$ with $n$ distinct principal curvatures, parametrized by lines of curvature with vanishing Laplace invariants. (1) If the lines of curvature are planar, then there are no such hypersurfaces for $n{\geq}4$, and for $n=3$, they are, up to M$\ddot{o}$bius transformations Dupin hypersurfaces with constant M$\ddot{o}$bius curvature. (2) If the principal curvatures are given by a sum of functions of separated variables, there are no such hypersurfaces for $n{\geq}4$, and for $n=3$, they are, up to M$\ddot{o}$bius transformations, Dupin hypersurfaces with constant M$\ddot{o}$bius curvature.

• Journal of the Optical Society of Korea
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• v.18 no.6
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• pp.746-752
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• 2014

In a multi-camera measurement system, the determination of the external parameters is one of the vital tasks, referred to as the calibration of the system. In this paper, a new geometrical calibration method, which is based on the theory of the vanishing line, is proposed. Using a planar target with three equally spaced parallel lines, the normal vector of the target plane can be confirmed easily in every camera coordinate system of the measurement system. By moving the target into more than two different positions, the rotation matrix can be determined from related theory, i.e., the expression of the same vector in different coordinate systems. Moreover, the translation matrix can be derived from the known distance between the adjacent parallel lines. In this paper, the main factors effecting the calibration are analyzed. Simulations show that the proposed method achieves robustness and accuracy. Experimental results show that the calibration can reach 1.25 mm with the range about 0.5m. Furthermore, this calibration method also can be used for auto-calibration of the multi-camera mefasurement system as the feature of parallels exists widely.