• 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.15 no.12
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• pp.1430-1441
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• 2012

In this paper, we present an algorithm that quickly and effectively estimates orthogonal vanishing points in equirectangular images of urban environment. Our algorithm is based on the RANSAC (RANdom SAmple Consensus) algorithm and on the characteristics of the line segment in the spherical panorama image of the $360^{\circ}$ longitude and $180^{\circ}$ latitude field of view. These characteristics can be used to reduce the geometric ambiguity in the line segment classification as well as to improve the robustness of vanishing point estimation. The proposed algorithm is validated experimentally on a wide set of images. The results show that our algorithm provides excellent levels of accuracy for the vanishing point estimation as well as line segment classification.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.5
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• pp.463-470
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• 2015

When a camera installed on a vehicle is used, estimation of the camera pose including tilt, roll, and pan angle with respect to the world coordinate system is important to associate camera coordinates with world coordinates. Previous approaches using huge calibration patterns have the disadvantage that the calibration patterns are costly to make and install. And, previous approaches exploiting multiple vanishing points detected in a single image are not suitable for automotive applications as a scene where multiple vanishing points can be captured by a front camera is hard to find in our daily environment. This paper proposes a camera pose estimation method. It collects multiple images of lane markings while changing the horizontal angle with respect to the markings. One vanishing point, the cross point of the left and right lane marking, is detected in each image, and vanishing line is estimated based on the detected vanishing points. Finally, camera pose is estimated from the vanishing line. The proposed method is based on the fact that planar motion does not change the vanishing line of the plane and the normal vector of the plane can be estimated by the vanishing line. Experiments with large and small tilt and roll angle show that the proposed method outputs accurate estimation results respectively. It is verified by checking the lane markings are up right in the bird's eye view image when the pan angle is compensated.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.53-62
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• 2013

This paper proposes a method of automatically predicting the vanishing point for the purpose of detecting the road region from natural images. The proposed method stably detects the vanishing point in the road environment by analyzing the dominant orientation of the image and predicting the vanishing point to be at the position where the feature components of the image are concentrated. For this purpose, in the first stage, the image is partitioned into sub-blocks, an edge sample is selected randomly from within the sub-block, and RANSAC is applied for line fitting in order to analyze the dominant orientation of each sub-block. Once the dominant orientation has been detected for all blocks, we proceed to the second stage and randomly select line samples and apply RANSAC to perform the fitting of the intersection point, then measure the cost of the intersection model arising from each line and we predict the vanishing point to be located at the average point, based on the intersection point model with the highest cost. Lastly, quantitative and qualitative analyses are performed to verify the performance in various situations and prove the efficiency of the proposed algorithm for detecting the vanishing point.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.5
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• pp.637-642
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• 2011

In this paper, a new technique to estimate the distances between a vanishing point and moving objects is proposed. A vanishing point for an input image is estimated and it use to extract distance form the vanishing point to a moving object. Using the obtained distances, moving objects is extracted. In simulation results, several performances for a test image sequnce is shown.

• 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 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.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.3
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• pp.20-31
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• 2008

In this paper, we propose a single view-based technique for the estimation of human height and position. Conventional techniques for the estimation of 3D geometric information are based on the estimation of geometric cues such as vanishing point and vanishing line. The proposed technique, however, back-projects the image of moving object directly, and estimates the position and the height of the object in 3D space where its coordinate system is designated by a marker. Then, geometric errors are corrected by using geometric constraints provided by the marker. Unlike most of the conventional techniques, the proposed method offers a framework for simultaneous acquisition of height and position of an individual resident in the image. The accuracy and the robustness of our technique is verified on the experimental results of several real video sequences from outdoor environments.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.126-137
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• 1997

This paper describes the algorithm which can estimate road following direction and deetect obstacle using a monocular vision system. This algorithm can estimate the course of vehicle using the vanishing point properties and detect obstacle by statistical method. The proposed algorithm is composed of four steps, which are lane prediction, lane extraction, road following parameter estimation and obstacle detection. It is designed for high processing speed and high accuracy. The former is achieved by a small area named sub-windown in lane existence area, the later is realized by using connected edge points of lane. We would like to present that the new mehod can detect obstacle using the simple statistical method. The paracticalities of the processing speed, the accuracy of the algorithm and proposing obstacle detection method, have been justified through the experiment applied VTR image of the real road to the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1259-1263
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• 2004

This paper describes position estimation algorithm using neural network for the navigation of the vision-based wheeled mobile robot (WMR) in a corridor with taking ceiling lamps as landmark. From images of a corridor the lamp's line on the ceiling in corridor has a specific slope to the lateral position of the WMR. The vanishing point produced by the lamp's line also has a specific position to the orientation of WMR. The ceiling lamps have a limited size and shape like a circle in image. Simple image processing algorithms are used to extract lamps from the corridor image. Then the lamp's line and vanishing point's position are defined and calculated at known position of WMR in a corridor. To estimate the lateral position and orientation of WMR from an image, the relationship between the position of WMR and the features of ceiling lamps have to be defined. But it is hard because of nonlinearity. Therefore, data set between position of WMR and features of lamps are configured. Neural network are composed and learned with data set. Back propagation algorithm(BPN) is used for learning. And it is applied in navigation of WMR in a corridor.