• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.239-242
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• 2003

In this paper, we propose how to determine the optimal camera position using inverse kinematics of virtual link model and manipulability measure. We model the variable distance and viewing direction between a target object and a camera position as a virtual link. And, by using inverse kinematics of virtual link model, we find out regions that satisfy the direction and distance constraints for the observation of target object. The solution of inverse kinematics of virtual link model simultaneously satisfies camera accessibility as well as a direction and distance constraints. And we use a manipulability measure of active camera system in order to determine an optimal camera position among the multiple solutions of inverse kinematics. By using the inverse kinematics of virtual link model and manipulability measure, the optimal camera position in order to observe a target object can be determined easily and rapidly.

• 제어로봇시스템학회논문지
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• 제16권3호
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• pp.269-276
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• 2010

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

• 한국지능시스템학회논문지
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• 제11권5호
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• pp.413-418
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• 2001

본 논문에서는 기존에 사용한 물리적 카메라 모델 대신 퍼지 모델을 사용한 새로운 카메라 보정 방식을 제안한다. 카메라 보정은 카메라의 영상 좌표계와 실제 환경이 가지는 좌표계와의 관계를 규정하는 것으로, 퍼지 모델을 이용하는 방법은 기존의 방법에서 이용했던 물리적 변수들을 설정할 수는 없지만 카메라 보정의 목적인 카메라 좌표계와 실제 환경 좌표계와의 관계를 별다른 제약없이 규정할 수 있으므로 매우 간단하고 효율적인 카메라 보정 방법이다. 실제 실험을 통해 얻은 실공간상의 하나의 보정면 좌표에 대해 퍼지 모델링 방법을 이용하여 3차원 실 공간 좌표 및 2차원 영상좌표 예측을 통해 제안한 방법의 유효성을 보인다.

• 한국전자통신학회논문지
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• 제8권12호
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• pp.1923-1932
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• 2013

본 논문에서는 광각 카메라로 촬영한 영상에서 생기는 영상의 왜곡 문제를 해결하기 위하여, 카메라 모델의 추정과 검증을 통하여 광각 카메라 영상에서의 방사형 왜곡을 보정하는 방법을 제안한다. 먼저, 교정 패턴으로부터 렌즈의 초점 거리와 주점의 위치 등으로 구성된 내부 파라미터와 회전과 위치 정보로 구성된 외부 파라미터를 찾음으로써 카메라의 왜곡 모델을 추정한다. 다음으로, 추정된 카메라 모델로부터 역으로 코너점을 다시 추출하여 검증하고, 마지막으로, 검증된 카메라 모델을 이용하여 영상의 왜곡을 보정한다. 일반 웹 카메라와 광각 카메라로부터 격자 모양의 교정 패턴을 촬영한 영상을 이용하여 방사형 왜곡을 보정하는 실험을 수행한 결과, 제안된 방법이 80% 이상의 개선 성능을 보임을 확인하였다.

• 센서학회지
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• 제13권1호
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• pp.27-34
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• 2004

Camera calibration is an important and fundamental procedure for the application of a vision sensor to 3D problems. Recently many camera calibration methods have been proposed particularly in the area of robot vision. However, the reliability of data used in calibration has been seldomly considered in spite of its importance. In addition, a camera model can not guarantee good results consistently in various conditions. This paper proposes methods to overcome such uncertainty problems of data and camera models as we often encounter them in practical camera calibration steps. By the use of the RANSAC (Random Sample Consensus) algorithm, few data having excessive magnitudes of errors are excluded. Artificial neural networks combined in a two-step structure are trained to compensate for the result by a calibration method of a particular model in a given condition. The proposed methods are useful because they can be employed additionally to most existing camera calibration techniques if needed. We applied them to a linear camera calibration method and could get improved results.

• 한국정보통신학회논문지
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• 제22권2호
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• pp.261-269
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• 2018

본 논문은 플렌옵틱 영상에 CCD 카메라 모델을 적용하여 플렌옵틱 카메라의 내부 파라미터를 구하는 편리한 방법을 제안한다. 플렌옵틱 카메라 캘리브레이션에 사용되는 영상은 일반적으로 CCD 카메라 캘리브레이션에서 사용하는 체크보드패턴을 사용한다. CCD 카메라 모델에 기반 하여 플렌옵틱 카메라 모델의 행렬식을 구하고 이를 통해 초점거리, 주점, 베이스라인, 가상카메라와 물체사이의 거리를 나타내는 4가지 방정식을 공식화한다. 그리고 비선형 최적화 기법을 수행하여 방정식의 해를 찾는다. 구해진 추정치는 실제 매개 변수와 비교하고 구해진 파라미터를 이용해 재 투영 오차율을 구한다. 실험 결과 제안한 방법을 통해 구한 매개 변수는 실제와 매우 유사한 값을 가지며 재투영 오차율은 수용할 수 있는 정도로 나타난다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.518-523
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• 2002

Nowadays the Rational Function Model (RFM), an abstract sensor model, is substituting physical sensor models for highly complicated imaging geometry. But RFM is algorithm to be required many Ground Control Points (GCP). In case of RFM of the third order, At least forty GCP are required far RFM generation. The purpose of this study is to research more efficient algorithm on GCP and accurate algorithm similar to RFM. The Polynomial Camera Model is relatively accurate and requires a little GCP in comparisons of RFM. This paper introduces how to generate Polynomial Camera Model and fundamental algorithms for construction of 3-D topographic data using the Polynomial Camera Model information in the Kompsat stereo pair and describes how to generate the 3-D ground coordinates by manual matching. Finally we tried to extract height information for the whole image area with the stereo matching technique based on the correlation.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.543-547
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• 2002

In this paper, we introduce a more improved camera modeling method for linear pushbroom images than the method proposed by Orun and Natarajan(ON). ON model shows an accuracy of within 1 pixel if more than 10 ground control points(GCPs) are provided. In general, there is high correlation between platform position and attitude parameters but ON model ignores attitude variation in order to overcome such correlation. We propose a new method that obtains an optimal solution set of parameters without ignoring the attitude variation. We first assume that attitude parameters are constant and estimate platform position's. Then we estimate platform attitude parameters using the values of estimated position parameters. As a result, we can set up an accurate camera model for a linear pushbroom satellite scene. In particular, we can apply the camera model to its surrounding scenes because our model provide sufficient information on satellite's position and attitude not only for a single scene but also for a whole imaging segment. We tested on two images: one with a pixel size 6.6m$\times$6.6m acquired from EOC(Electro Optical Camera), and the other with a pixel size 10m$\times$l0m acquired from SPOT. Our camera model procedures were applied to the images and gave satisfying results. We had obtained the root mean square errors of 0.5 pixel and 0.3 pixel with 25 GCPs and 23 GCPs, respectively.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2923-2925
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• 1999

The objective of camera calibration is to determine the internal optical characteristics of camera and 3D position and orientation of camera with respect to the real world. Calibration procedure applicable to general purpose cameras and lenses. The general method to revise the accuracy rate of calibration is using mathematical distortion of lens. The effective og calibration show big difference in proportion to distortion of camera lens. In this paper, we propose the method which calibration distortion model by using neural network. The neural network model implicity contains all the distortion model. We can predict the high accuracy of calibration method proposed in this paper. Neural network can set properly the distortion model which has difficulty to estimate exactly in general method. The performance of the proposed neural network approach is compared with the well-known Tsai's two stage method in terms of calibration errors. The results show that the proposed approach gives much more stable and acceptabke calibration error over Tsai's two stage method regardless of camera resolution and camera angle.

• 한국항해학회지
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• 제16권3호
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• pp.77-85
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• 1992

Camera arrangement for depth and image correspondence is very important to the computer vision. Two conventional comera arrangements for stereo computer vision are lateral model and axial motion model. In this paper, using the axial motion stereo camera model, the algorithm for camera focal length measurement and the surface smoothness with the radiance-irradiance is proposed fro 3-dimensional image correspondence on stereo computer vision. By adapting the above algorithm, camera focal length can be measured precisely and the resolution of 3-dimensional image correspondence has been improved comparing to that of the axial motion model without the radiance-irradiance relation.