• East Asian mathematical journal
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• v.22 no.1
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• pp.111-130
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• 2006

We study cosymplectic submersions with vanishing cosymplectic Bochner curvature tensor.

• Journal of the Chungcheong Mathematical Society
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• v.34 no.1
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• pp.93-107
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• 2021

We study the Gromov-Witten invariants of $\underbrace{{\mathbb{P}}^1\;{\times}\;{\mathbb{P}}^1\;{\times}\;{\ldots}\;{\times}\;{\mathbb{P}}^1}_{n}.$ Our main result is a proof of some vanishing conditions on the Gromov-Witten invariants of ℙ1 × ℙ1 × … × ℙ1 in all genera.

• Bulletin of the Korean Mathematical Society
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• v.60 no.3
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• pp.623-636
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• 2023

In this paper, we prove some vanishing theorems under the assumptions of weighted BiRic curvature or m-Bakry-Émery-Ricci curvature bounded from below.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.147-154
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• 2004

In this paper, Inverted Coordinates Image Space (ICIS) is proposed as a solution for the problem of the unbounded accumulator space in the automatic detection of the finite/infinite vanishing points in image space. Since the ICIS is based on the direct transformation from the image space, it does not lose any geometrical information from the original image and it does not require camera calibration as opposed to the Gaussian sphere based methods. Moreover, the proposed method can accurately detect both the finite and infinite vanishing points under a small fixed memory amount as opposed to the conventional image space based methods. Experiments are conducted on various real images in architectural environments to show the advantages of the proposed approach over conventional methods.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.413-422
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• 2013

In this paper, we propose a novel method for real-time lane detection that is robust for inclined roads and not require a camera parameter, the Inverse Perspective Transform of the image, and the proposed lane filter. After finding the vanishing point from the start frame of the image and storing the region surrounding the vanishing point as the Template Area(TA), our method predict the lanes by scanning toward the lower part from the vanishing point of the image and obtain the image removed the perspective effect using the Inverse Perspective Transform coefficients extracted based on the predicted lanes. To robustly determine lanes on inclined roads, the region surrounding the vanishing point is set up as the template area (TA), and, by recalculating the vanishing point by tracing the area similar to the TA (SA) in the input image through template matching, it responds to the changes on the road conditions. The proposed method for a more robust lane detection method for inclined roads is a lane detection method by applying a lane detection filter on an image removed of the perspective effect. Through this method, the processing region is reduced and the processing procedure is simplified to produce a satisfactory lane detection result of about 40 frames per second.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.407-420
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• 2021

Deep neural networks are widely used to solve various problems. However, the deep neural network with a deep hidden layer frequently has a vanishing gradient or exploding gradient problem, which is a major obstacle to learning the deep neural network. In this paper, we propose a parametric activation function to alleviate the vanishing gradient problem that can be caused by nonlinear activation function. The proposed parametric activation function can be obtained by applying a parameter that can convert the scale and location of the activation function according to the characteristics of the input data, and the loss function can be minimized without limiting the derivative of the activation function through the backpropagation process. Through the XOR problem with 10 hidden layers and the MNIST classification problem with 8 hidden layers, the performance of the original nonlinear and parametric activation functions was compared, and it was confirmed that the proposed parametric activation function has superior performance in alleviating the vanishing gradient.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.93-101
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• 2023

In this paper, we propose a method of creating a 3D bounding box for an object using a vanishing point to increase the accuracy of object recognition in an image when recognizing an traffic object using a video camera. Recently, when vehicles captured by a traffic video camera is to be detected using artificial intelligence, this 3D bounding box generation algorithm is applied. The vertical vanishing point (VP1) and horizontal vanishing point (VP2) are derived by analyzing the camera installation angle and the direction of the image captured by the camera, and based on this, the moving object in the video subject to analysis is specified. If this algorithm is applied, it is easy to detect object information such as the location, type, and size of the detected object, and when applied to a moving type such as a car, it is tracked to determine the location, coordinates, movement speed, and direction of each object by tracking it. Able to know. As a result of application to actual roads, tracking improved by 10%, in particular, the recognition rate and tracking of shaded areas (extremely small vehicle parts hidden by large cars) improved by 100%, and traffic data analysis accuracy was improved.

• Journal of the Korean Mathematical Society
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• v.55 no.5
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• pp.1103-1129
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• 2018

In this paper, we show several vanishing type theorems for p-harmonic ${\ell}$-forms on Riemannian manifolds ($p{\geq}2$). First of all, we consider complete non-compact immersed submanifolds $M^n$ of $N^{n+m}$ with flat normal bundle, we prove that any p-harmonic ${\ell}$-form on M is trivial if N has pure curvature tensor and M satisfies some geometric conditions. Then, we obtain a vanishing theorem on Riemannian manifolds with a weighted $Poincar{\acute{e}}$ inequality. Final, we investigate complete simply connected, locally conformally flat Riemannian manifolds M and point out that there is no nontrivial p-harmonic ${\ell}$-form on M provided that the Ricci curvature has suitable bound.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.508-510
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• 2004

In this paper, we proposed the GPS position data correction method for autonomous land navigation using vanishing point property and a monocular vision system. Simulations are carried out over driving distances of approximately 60 km on the basis of realistic road data. In straight road, the proposed method reduces GPS position error to minimum more than 63% and positioning errors within less than 0.5m are observed. However, the average accuracy of the method is not presented. because it is difficult to estimate it in curve road or other road environments.