• 한국정보통신학회논문지
• /
• 제3권4호
• /
• pp.867-873
• /
• 1999

카메라 교정(Camera calibration)은 알고있는 월드 좌표계(world coordinate system)의 제어점(control points)들에 대하여 카메라의 내부/외부 인자(internal and external parameters)들을 계산하는 과정이다. 정확한 카메라 교정은 정밀한 측정을 위해서 반드시 요구된다. 본 논문에서, 우리는 3D 기하학이나 카메라 광학에 대한 특별한 지식을 요구하지 않는 신경망을 이용하여 간단하면서도 유연한 카메라 교정을 제안한다. 제안한 방법은 내부/외부 인자를 요구하지 않는 응용 분야에 매우 유용하다. 또한 제안한 카메라 교정은 물체가 이미지 평면과 거의 평행할 경우에 발생하는 악조건(ill-condition)문제를 해결할 수 있는 장점을 가졌다. 이러한 악조건은 시각 시스템을 이용하여 제품 검사를 할 경우에 흔히 발생한다. 좀더 정확한 교정을 위해 획득한 이미지는 렌즈의 방사형 왜곡에 따라 두 개의 지역으로 분할하여 교정된다. 그리고 Tsai의 알고리즘을 이용한 결과와 제안한 방법을 이용하여 교정한 결과를 실험을 통해 타당성을 증명한다.

• 한국측량학회지
• /
• 제32권3호
• /
• pp.205-216
• /
• 2014

Multi-camera systems have been widely used as cost-effective tools for the collection of geospatial data for various applications. In order to fully achieve the potential accuracy of these systems for object space reconstruction, careful system calibration should be carried out prior to data collection. Since the structural integrity of the involved cameras' components and system mounting parameters cannot be guaranteed over time, multi-camera system should be frequently calibrated to confirm the stability of the estimated parameters. Therefore, automated techniques are needed to facilitate and speed up the system calibration procedure. The automation of the multi-camera system calibration approach, which was proposed in the first part of this paper, is contingent on the automated detection, localization, and identification of the object space signalized targets in the images. In this paper, the automation of the proposed camera calibration procedure through automatic target extraction and labelling approaches will be presented. The introduced automated system calibration procedure is then implemented for a newly-developed multi-camera system while considering the optimum configuration for the data collection. Experimental results from the implemented system calibration procedure are finally presented to verify the feasibility the proposed automated procedure. Qualitative and quantitative evaluation of the estimated system calibration parameters from two-calibration sessions is also presented to confirm the stability of the cameras' interior orientation and system mounting parameters.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제52권10호
• /
• pp.569-574
• /
• 2003

Computer vision system is broadly adapted like as autonomous vehicle system, product line inspection, etc., because it has merits which can deal with environment flexibly. However, for applying it for that industry, it has to clear the problem that recognize position parameter of itself. So that computer vision system stands in need of camera calibration to solve that. Camera calibration consists of the intrinsic parameter which describe electrical and optical characteristics and the extrinsic parameter which express the pose and the position of camera. And these parameters have to be reorganized as the environment changes. In traditional methods, however, camera calibration was achieved at off-line condition so that estimation of parameters is in need again. In this paper, we propose a method to the calibration of camera using line correspondence in image sequence varied environment. This method complements the corresponding errors of the point corresponding method statistically by the extraction of line. The line corresponding method is strong by varying environment. Experimental results show that the error of parameter estimated is within 1% and those is effective.

• 제어로봇시스템학회논문지
• /
• 제8권9호
• /
• pp.803-811
• /
• 2002

Machine vision systems are usually used to identify traffic lanes and then determine the steering angle of an autonomous vehicle in real time. The steering angle is calculated using a geometric model of various parameters including the orientation, position, and hardware specification of a camera in the machine vision system. To find the accurate values of the parameters, camera calibration is required. This paper presents a new camera-calibration algorithm using known traffic lane features, line thickness and lane width. The camera parameters considered are divided into two groups: Group I (the camera orientation, the uncertainty image scale factor, and the focal length) and Group II(the camera position). First, six control points are extracted from an image of two traffic lines and then eight nonlinear equations are generated based on the points. The least square method is used to find the estimates for the Group I parameters. Finally, values of the Group II parameters are determined using point correspondences between the image and its corresponding real world. Experimental results prove the feasibility of the proposed algorithm.

• 제어로봇시스템학회논문지
• /
• 제21권7호
• /
• pp.602-608
• /
• 2015

This paper proposes an algorithm for precise detection of corner points on a coplanar checkerboard in order to perform stereo camera calibration using a single frame. Considering the conditions of automobile production lines where a stereo camera is attached to the windshield of a vehicle, this research focuses on a coplanar calibration methodology. To obtain the accurate values of the stereo camera parameters using the calibration methodology, precise localization of a large number of feature points on a calibration target image should be ensured. To realize this demand, the idea with respect to a checkerboard pattern design and the use of a Homography matrix are provided. The calibration result obtained by the proposed method is also verified by comparing the depth information from stereo matching and a laser scanner.

• 한국콘텐츠학회논문지
• /
• 제10권6호
• /
• pp.10-17
• /
• 2010

본 논문은 전방향 스테레오 카메라를 이용한 거리 측정을 위해 반드시 수행되어야 하는 전방향 카메라의 교정방법에 관해 기술한다. 전방향 스테레오 카메라의 교정에 있어서, 두 대의 전방향 카메라를 각각 독립적으로 교정하거나 두 대의 카메라의 베이스라인이 크지 않은 경우의 교정은 기존의 연구된 다양한 방법을 통해 가능하다. 그러나 전방향 스테레오 카메라를 이용하여 원거리를 측정하기 위해서는 베이스라인이 충분히 커야 하며, 충분히 큰 베이스라인은 두 대의 전방향 카메라를 동시에 교정하는 것이 매우 힘들다. 이는 두 대의 전방향 카메라에서 촬영된 교정을 위한 테스트패턴의 크기가 최소한 한 대의 전방향 카메라에서 매우 작은 크기로 나타나기 때문이다. 따라서 본 논문에서는 베이스라인이 큰 두 대의 전방향 카메라의 교정을 위한 방법을 제안하고 실험을 통해 검증한다.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1997년도 추계학술대회 논문집
• /
• pp.225-229
• /
• 1997

This paper describes the camera calibration based-neural network with a camera modeling that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortion. Radial distortion causes and inward or outward displacement of a given image point from its ideal location. Actual optical systems are subject to various degrees of decentering, that is, the optical centers of lens elements are not strictly collinear. Thin prism distortion arises from imperfection in lens design and manufacturing as well as camera assembly. It is our purpose to develop the vision system for the pattern recognition and the automatic test of parts and to apply the line of manufacturing. The performance of proposed camera calibration is illustrated by simulation and experiment.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
• /
• pp.56-58
• /
• 2006

Camera calibration in machine vision is the process of determining the intrinsic cameara parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• 센서학회지
• /
• 제28권3호
• /
• pp.182-186
• /
• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.371-374
• /
• 1997

Recently, the development of computer achieves a system which is similar to the mechanics of human visual system. The 3-dimensional measurement using monocular vision system must be achieved a camera calibration. So far, the camera calibration technique required reference target in a scene. But, these methods are inefficient because they have many calculation procedures and difficulties in analysis. Therefore, this paper proposes a native method that without reference target in a scene. We use the grid type frame with different line widths. This method uses vanishing point concept that possess a rotation parameter of the camera and perspective ration that perspect each line widths into a image. We confirmed accuracy of calibration parameter estimation through experiment on the algorithm with a grid paper with different line widths.