• 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.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.231-239
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• 2018

In this paper, we propose a vanishing point detection method using multiple initial vanishing points. Vanishing points are important geometric information that is used for reconstructing 3D structures. Three vanishing points are detected for indoor scenes. In the previous work, it could be inaccurate to detect only one initial vanishing point, because initial vanishing point getting most highest sum of voting could be deferent from the best initial vanishing point. Therefore the method which sets multiple initial vanishing point and detects a best vanishing point from them gives us preparation for the prior case. Also in this paper, we propose a adjusting vanishing point method by postprocessing of detected vanishing points. We could detect more accurate vanishing point by using postprocessing. Experimental results show that the accuracy of the vanishing point detection is about 1~2% higher than that of the existing method through the proposed method and the performance is improved accordingly.

• 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 the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.477-483
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• 2019

In this paper, we propose vanishing-point position-improvement algorithms by using multiple images and a homography matrix. Vanishing points can be detected from a single image, but the positions of detected vanishing points can be improved if we adjust their positions by using information from multiple images. More accurate indoor space information detection is possible through vanishing points with improved positional accuracy. To adjust a position, we take three images and detect the information, detect the homography matrix between the walls of the images, and convert the vanishing point positions using the detected homography. Finally, we find an optimal position among the converted vanishing points and improve the vanishing point position. The experimental results compared an existing algorithm and the proposed algorithm. With the proposed algorithm, we confirmed that the error angle to the vanishing point position was reduced by about 1.62%, and more accurate vanishing point detection was possible. In addition, we can confirm that the layout detected by using improved vanishing points through the proposed algorithm is more accurate than the result from the existing algorithm.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1145-1148
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• 2002

This paper presents a new method which is calculated to use only three vanishing points in order to compute the dimensions of object and its pose from a single image of perspective projection taken by a camera and the problem of recovering 3D models from three vanishing points of box scene. Our approach is to compute only three vanishing points without this information such as the focal length, rotation matrix, and translation from images in the case of perspective projection. We assume that the object can be modeled as a linear function of a dimension vector ν. The input of reconstruction is a set of correspondences between features in the model and features in the image. To minimize each the dimensions of the parameterized models, this reconstruction of optimization can be solved by the standard nonlinear optimization techniques with a multi-start method which generates multiple starting points for the optimizer by sampling the parameter space uniformly.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.1-10
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• 2013

This paper proposes a method to detect vanishing points of an indoor scene using line segment classification. Two-stage vanishing points detection is carried out to detect vanishing point in indoor scene efficiently. In the first stage, the method examines whether the image composition is a one-point perspective projection or a two-point one. If it is a two-point perspective projection, a horizontal line through the detected vanishing point is found for line segment classification. In the second stage, the method detects two vanishing points exactly using line segment classification. The method is evaluated by synthetic images and an image DB. In the synthetic image which some noise is added in, vanishing point detection error is under 16 pixels until the percent of the noise to the image becomes 60%. Vanishing points detection ratio by A.Quattoni and A.Torralba's image DB is over 87%.

• ETRI Journal
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• v.28 no.6
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• pp.819-821
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• 2006

Log-polar coordinate image space is proposed as a solution for the problem of unbounded accumulator space in the automatic detection of vanishing points. The proposed method can detect vanishing points at high speed under small memory requirements, as opposed to conventional image space based methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.155-156
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• 2009

• 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 KIISE:Computer Systems and Theory
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• v.30 no.11
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• pp.621-628
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• 2003

This paper addresses a method of enabling objects in an image to have apparent 3D motion. Many researchers have solved this issue by reconstructing 3D model from several images using image-based modeling techniques, or building a cube-modeled scene from camera calibration using vanishing points. This paper, however, presents the possibility of image-based motion without exact 3D information of scene geometry and camera calibration. The proposed system considers the image plane as a projective plane with respect to a view point and models a 2D frame of a projected 3D object using only lines and points. And a modeled frame refers to its vanishing points as local coordinates when it is transformed.