• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.752-757
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• 2003

Recently, 3D structure recovery through self-calibration of camera has been actively researched. Traditional calibration algorithm requires known 3D coordinates of the control points while self-calibration only requires the corresponding points of images, thus it has more flexibility in real application. In general, self-calibration algorithm results in the nonlinear optimization problem using constraints from the intrinsic parameters of the camera. Thus, it requires initial value for the nonlinear minimization. Traditional approaches get the initial values assuming they have the same intrinsic parameters while they are dealing with the situation where the intrinsic parameters of the camera may change. In this paper, we propose new initialization method using the minimum 2 images. Proposed method is based on the assumption that the least violation of the camera’s intrinsic parameter gives more stable initial value. Synthetic and real experiment shows this result.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.379-382
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• 2002

Among the CMM calibration techniques, the calibration with standard specimen is most accurate way to acquire the required precision. When there is no standard specimen, the calibration of CMM with itself is possible. This calibration method is called "self-calibration". In this paper, we developed self-calibration algorithm for CMM XY plane. It is possible to calculate the in-plane error and out-of-plane error of CMM with 3 different measurement of same artifact. Experimental result shows that the non-orthogonality error is dominant in in-plane error and the self-calibration result and laser interferometer measured result have almost same value.ame value.

• Journal of Ocean Engineering and Technology
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• v.35 no.3
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• pp.183-190
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• 2021

Tomographic particle image velocimetry (PIV) is a widely used method that measures a three-dimensional (3D) flow field by reconstructing camera images into voxel images. In 3D measurements, the setting and calibration of the camera's mapping function significantly impact the obtained results. In this study, a camera self-calibration technique is applied to tomographic PIV to reduce the occurrence of errors arising from such functions. The measured 3D particles are superimposed on the image to create a disparity map. Camera self-calibration is performed by reflecting the error of the disparity map to the center value of the particles. Vortex ring synthetic images are generated and the developed algorithm is applied. The optimal result is obtained by applying self-calibration once when the center error is less than 1 pixel and by applying self-calibration 2-3 times when it was more than 1 pixel; the maximum recovery ratio is 96%. Further self-correlation did not improve the results. The algorithm is evaluated by performing an actual rotational flow experiment, and the optimal result was obtained when self-calibration was applied once, as shown in the virtual image result. Therefore, the developed algorithm is expected to be utilized for the performance improvement of 3D flow measurements.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.391-394
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• 2005

In this paper, we present an approach that is able to reconstruct 3 dimensional metric models from un-calibrated images acquired by a freely moved camera system. If nothing is known of the calibration of either camera, nor the arrangement of one camera which respect to the other, then the projective reconstruction will have projective distortion which expressed by an arbitrary projective transformation. The distortion on the reconstruction is removed from projection to metric through self-calibration. The self-calibration requires no information about the camera matrices, or information about the scene geometry. Self-calibration is the process of determining internal camera parameters directly from multiply un-calibrated images. Self-calibration avoids the onerous task of calibrating cameras which needs to use special calibration objects. The root of the method is setting a uniquely fixed conic(absolute quadric) in 3D space. And it can make possible to figure out some way from the images. Once absolute quadric is identified, the metric geometry can be computed. We compared reconstruction image from calibrated images with the result by self-calibration method.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.48-52
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• 2001

In this paper presents an accurate AFM used that is free from the Z-directional distortion of a servo actuator is described. Two mathematical correction methods by the in-situ self-calibrationare employed in this AFM. One is the method by the integration, and the other is the method by inverse function of the calibration curve. The in situ self-calibration method by the integration, the derivative of the calibration curve function of the PZT actuator is calculated from the profile measurement data sets which are obtained by repeating measurements after a small Z-directional shift. Input displacement at each sampling point is approximately estimated first by using a straight calibration line. The derivative is integrated with reference to the approximate input to obtain the approximate calibration curve. Then the approximation of the input value of each sampling point is improved using the obtained calibration curve. Next the integral of the derivative is improved using the newly estimated input values. As a result of repeating these improving process, the calibration curve converges to the correct one, and the distortion of the AFM image can be corrected. In the in situ self-calibration through evaluating the inverse function of the calibration curve, the profile measurement data sets were used during the data processing technique. Principles and experimental results of the two methods are presented.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.41-52
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• 2018

This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.2
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• pp.11-16
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• 2001

This paper presents an in self-calibration method to corrent the Z-directional distortion of AFM(Atomic Force Microscopy).

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2435-2439
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• 2005

In case of visual sensing systems with multiple mirrors, systematic errors need to be reduced by the system calibration and the mirror position adjustment in order to enhance system measurement accuracy. In this paper, a self calibration method is presented for a visual sensing system designed to measure the three-dimensional information in deformable peg-in-hole tasks. It is composed of a CCD camera and a series of mirrors including two pyramidal mirrors. By using an image of the inner pyramidal mirror taken by the system, the error parameters of the inner pyramidal mirror could be calibrated or adjusted. Also the influence of the plane mirrors is investigated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.536-544
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• 2017

Wireless RSSI (Received Signal Strength Indication) fingerprinting is one of the most popular methods for indoor positioning as it provides reasonable accuracy while being able to exploit existing wireless infrastructure. However, the process of radio map construction (aka fingerprint calibration) is laborious and time consuming as precise physical coordinates and wireless signals have to be measured at multiple locations of target environment. This paper proposes a method to build the map from a combination of RSSIs without location information collected in a crowdsourcing fashion, and a handful of labeled RSSIs using a semi-supervised self organizing map learning algorithm. Experiment on simulated data shows promising results as the method is able to recover the full map effectively with only 1% RSSI samples from the fingerprint database.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.5 s.170
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• pp.63-71
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• 2005

When an almost flat surface under test is measured by an interferometer, the measurement result is largely influenced by systematic errors that include geometrical errors of a reference flat surface. To determine the systematic errors of the interferometer by the conventional method that is called the three flat method, we must take the reference flat surface out from the interferometer and measure it. Because of difficulties to set the reference flat surface to the interferometer exactly and quickly, this method is not practical. On the other hand, the method that measures a surface under test with some shifts in the direction being perpendicular to the optical axis of the interferometer is studied. However, the parasitic pitching, rolling and up-down movement caused by the above shifts brings serious error to the measurement result, and the algorithm by which the influences can be eliminated is not still established. In this paper, we propose the self-calibration algorithm for determining the systematic errors that include geometrical errors of a reference flat surface by several rotation shifts and a linear shift of general surface under test, and verify by a numerical experiment that this algorithm is useful for determining the systematic errors.