• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1891-1894
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• 2003

본 논문에서는 분할 영상 좌보계 (split image coordinate: SIC)를 제안하여 3차원 영상의 주요 특징 중의 하나인 유, 무한 소실점을 그 위치의 무한성이나 카메라의 보정과 관계없이 정확하게 자동 추출하였다. 제안한 방법에서는 가우시안 구 (Gaussian sphere) 기반의 기존 방법들과는 달리 영상 공간을 누적 공간으로 활용함으로써 카메라 보정이나 영상의 사전정보가 없어도 원 영상의 정보 손실 없이 소실점을 추출할 수 있고, 영상을 무한대까지 확장한 후 분할하여 재정의 함으로써 유, 무한 소실점을 모두 추출할 수 있도록 하였다. 정확한 소실점의 검출을 위하여 직선 검출 과정에서는 방향성 마스크 (mask)를 사용하였으며, 직선들의 군집화 (clustering) 과정에서는 기울기 히스토그램 방법과 수평/수직 군집화 방법을 적응적으로 적용하였다. 제안한 방법을 합성 영상 및 건축물 (man-made environment) 영상에 적용시켜 유, 무한 소실점들을 효과적이고 정확하게 찾을 수 있음을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.834-841
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• 2020

Among the autonomous driving systems based on visual sensors, the control method using a vanishing point is the most general method for autonomous driving. However, if the lane is lost or does not exist, it is very difficult to detect this and estimate the vanishing point. In this paper, we predict the vanishing point of the road and the vanishing point lines on the left and right sides using CNN for the camera image and design the steering controller for autonomous driving from the predicted results. As a result of the simulation, it was confirmed that the proposed method well tracked the center of the road regardless of the presence or absence of a solid lane, and was superior to the control method using a general method using the vanishing point.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.357-359
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• 2010

In monocular images that are used to determine the depth of the vanishing point, the buildings, roads and buildings, such as outdoor video or hallway with room inside for the interior structure, such as the vanishing point in the video is a very strong depth cue. Depth map using the vanishing point in the three-dimensional space, the two-dimensional imaging is used to restore the structure. But if there is a vanishing point vanishing point in the video also depends on the location of the relative depth of different ways to express that need. In this paper we present images of a vanishing point with respect to the improved depth-map was created. Proposed an area where the loss of seven points and areas defined as areas along the proposed direction of different depth.

• 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 Korean Information Science Society Conference
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• 2004.04b
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• pp.742-744
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• 2004

본 논문에서는 두 개의 직교하는 소실점(Orthogonal Vanishing Points)을 이용하여 카메라의 내부 파라미터를 추정하기 위한 방법을 제안한다. 카메라 보정(camera calibration)은 2차원 영상으로부터 3차원 정보를 얻기 위한 중요한 단계이다. 기존의 소실점을 이용한 대부분의 방법들은 세 개의 직교하는 소실점을 사용하여 파라미터론 추정하지만, 실제 영상에서는 세 개의 직교 소실점을 포함하는 영상을 획득하는 것은 어려운 문제이다 따라서 본 논문에서는 2개의 직교 소실점을 사용하여 카메라 U부 보정을 위한 기하적이고 직관적인 새로운 방법을 제안한다. 주점(principal point)과 초점거리(focal length)는 Thales의 이론을 기초한 기하학적 제약사항으로부터 다중 반구(multiple hemispheres)들의 관계로부터 유도된다.

• 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.

• 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 Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.782-789
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• 2009

In this paper, we propose an algorithm for creating stereoscopic video from a monoscopic video. Parallel straight lines in a 3D space get narrower as they are farther from the perspective images on a 2D plane and finally meet at one point that is called a vanishing point. A viewer uses depth perception clues called a vanishing point which is the farthest from a viewer's viewpoint in order to perceive depth information from objects and surroundings thereof to the viewer. The viewer estimates the vanishing point with geometrical features in monoscopic images, and can perceive the depth information with the relationship between the position of the vanishing point and the viewer's viewpoint. In this paper, we propose a method to estimate a vanishing point with edge direction histogram in a general monoscopic image and to create a depth map depending on the position of the vanishing point. With the conversion method proposed through the experimental results, it is seen that stable stereoscopic conversion of a given monoscopic video is achieved.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.125-131
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• 2019

A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.451-453
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• 2012

2D영상을 3D영상으로 변환하기 위해서는 영상 내의 깊이 정보를 알 수 있는 깊이지도(depth map)가 필요하다. 깊이지도를 획득하기 위해 다양한 시각적 특성들이 이용되는데, 본 논문에서는 영상에서 아래는 가깝고 위는 멀다는 가정을 기반으로 하는 상대적 높이(relative height) 특성을 이용하여 깊이지도를 생성하는 방법을 제안한다. 기존의 상대적 높이 특성을 이용한 방법들은 왼쪽이나 오른쪽으로 거리가 멀어지는 영상에 대해서 부정확한 깊이지도를 생성한다. 따라서 제안하는 방법에서는 문제점을 개선하기 위해 입력 영상을 소실점 위치에 따라 분류하여 깊이지도를 생성한다. 소실점이 왼쪽이나 오른쪽에 위치할 때는 영상을 회전시켜 소실점을 상단으로 보내 상대적 높이 가정에 맞도록 영상을 변환하고 소실점이 중앙에 위치할 때는 하늘 검출 방법을 통해 실내, 실외를 판별한 후 각 특성에 맞는 초기 깊이 모델을 적용한다. 실험 결과, 제안하는 방법이 상대적 높이 가정을 만족하지 않는 영상도 적합한 깊이지도를 생성할 수 있음을 확인하였다.