• Journal of information and communication convergence engineering
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• 제9권6호
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• pp.747-752
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• 2011

The position detection of overlapping area in the interframe for image stitching using auto and cross correlation function (ACCF) and compounding one image with the stitching algorithm is presented in this paper. ACCF is used by autocorrelation to the featured area to extract the filter mask in the reference (previous) image and the comparing (current) image is used by crosscorrelation. The stitching is detected by the position of high correlation, and aligns and stitches the image in shifting the current image based on the moving vector. The ACCF technique results in a few computations and simplicity because the filter mask is given by the featuring block, and the position is enabled to detect a bit movement. Input image captured from CMOS is used to be compared with the performance between the ACCF and the window correlation. The results of ACCF show that there is no seam and distortion at the joint parts in the stitched image, and the detection performance of the moving vector is improved to 12% in comparison with the window correlation method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1998년도 춘계학술대회 논문집
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• pp.100-105
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• 1998

Image processing was used to recognize parts at various disposition. Non-transpatent tachometer panel for automobile and semi-transparent panel have been used as test specimen. Laplacian filter and various threshold values have been applied for preprocessing and edge following algorithm has been applied. Series of length data between edges have been generated from each image and compared using cross correlation coefficient. The result using cross correlation coefficient. The result using both edge following and cross correlation coefficient was proven to be the best fit for the proposed parts.

• 한국수자원학회논문집
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• 제38권10호
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• pp.851-859
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• 2005

정규 상호 상관 (상관계수)은 입자영상유속계(PIV) 분석에서 형태 분석을 위한 가장 정확하고 적합한 척도이다. 그러나 상관계수는 주파수 영역에서 그에 상당하는 간단한 수식 표현이 없기 때문에, 빠르지만 부정확한 척도들이 종종 이용된다. 이러한 척도 중에서 선정된 세 가지 방법과 상관계수법을 상호 비교하였다. 그 결과 상관계수법을 제외한 나머지 척도들은 모두 종종 부정확한 결과를 도출함을 알 수 있었다. 그러나 상관계수법은 계산 시간이 많이 걸린다는 단점을 지니고 있다. 이 문제를 해결하기 위해, 상관계수법을 계산하는 빠르고 정확한 방법을 제시하였다. 이 방법은 상관계수의 분산을 계산하는 Fn 알고리듬과 분모를 계산하는 순차가감법을 결합한 것이다. 시험 결과 이 방법은 상관계수를 빠르고 정확하게 계산할 수 있음을 보였다.

• 대한의용생체공학회:의공학회지
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• 제19권4호
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• pp.379-384
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• 1998

본 연구에서는 초음파를 이용하여 피부 혈관에서 그 위치에 대한 혈류 신호정보를 얻을 수 있는 펄스파(pulsed wave) 시스템을 이 분야에 응용하기 위한 가능성을 고찰하였다. 20MHz의 변환자로 수신된 신호들은 시간 영역에서 서로 비교하여 편이량을 cross-correlation방법을 사용하여 혈류속도를 구하였다. 피부의 매우 작은 혈관에서 혈류속도 거출시 발생되는 문제점에 대해 in-vitro와 in-vivo 실험을 통해서 이 방법의 효용성을 보이고 그 문제점에 대한 해결 방법을 제시한다. 시간 편이량 측정은 cross-correlation방법에 의해 정규화된 계수에서 최대점을 찾는 것이며 송신주기에 따라 반사된 수신 신호에서부터 cross-correlation방법을 사용하여 속도를 구한다. In-패패 실험을 통해 작은 튜브 내의 깊이에 대한 속도 정보를 주는 속도 profile과 이론적으로 계산된 속도 rpofile을 비교하였으며 토끼 귀 부분의 auriculares caudales에서 소동맥과 소정맥에 대한 혈류속도 profile을 구하였다. 수신 신호에서 진동에 의한 오차는 DFT를 사용하여 보정하였고 클루터 신호는 전체 수신 신호를 평균하여 하나의 기준 수신 신호를 정하여 이것에서 다른 수신 신호 값을 뺌으로써 감소시켰다.

• 한국가시화정보학회:학술대회논문집
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• 한국가시화정보학회 2003년도 추계학술대회 논문집
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• pp.63-66
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• 2003

A new algorithm, CBIR (Correlation Based Image Registration) was proposed to improve the resolution of image registration for PSP (Pressure Sensitive Paint). The local displacement vectors were obtained by finding the displacement which maximizes the cross-correlation between two interrogation windows of 'wind-off' and 'wind-on' images. A recursive multigrid processing was employed to increase the non-linear spatial resolutions. The variations of image were precisely measured without identifying the control points.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.1778-1782
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• 2004

A new algorithm, CBIR (Correlation-Based Image Registration) was proposed to improve the resolution of image registration for PSP (Pressure-Sensitive Paint). The local displacement vectors were obtained by finding the displacement which maximizes the cross-correlation between two interrogation windows of 'wind-off' and 'wind-on' images. A recursive multigrid processing was employed to increase the non-linear spatial resolutions. The variations of image were precisely measured without identifying the control points.

• 한국지능시스템학회논문지
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• 제20권4호
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• pp.548-553
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• 2010

본 논문에서는 4차원의 히스토그램 기반 상관성을 이용한 효과적인 모양영상의 인식방법을 제안하였다. 여기서 히스토그램 기반 상관성은 4개 방향을 고려한 계산으로 얻어지며, 이는 영상 사이에 대응하는 차원의 위치를 비교함으로써 유사성을 좀 더 정확하게 반영하기 위함이다. 또한 상관성 척도로 정규화된 상호상관계수를 이용함으로써 모양, 위치, 크기, 회전과 같은 기하학적 변화에 강건한 인식성능을 얻기 위함이다. 제안된 방법을 8개의 $64\times64$ 픽셀의 모양영상과 30개의 $256\times256$ 픽셀의 모양영상을 대상으로 실험한 결과, 영상의 속성을 잘 반영하는 우수한 인식성능이 있음을 확인하였다.

• 한국가시화정보학회:학술대회논문집
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• 한국가시화정보학회 2002년도 추계학술대회 논문집
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• pp.7-10
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• 2002

A novel two-phase PIV algorithm using a single camera has been proposed, which introduces a method of image-separation into respective phase images, and is applied to freely rising single bubble. Gas bubble, tracer particle and background each have different gray intensity ranges on the same image frame when reflection and dispersion in the phase interface are intrinsically eliminated by optical filters and fluorescent material. Further, the signals of the two phases do not interfere with each other. Gas phase velocities are obtained from the separated bubble image by applying the two-frame PTV. On the other hand, liquid phase velocities are obtained from the tracer particle image by applying the cross-correlation algorithm. Moreover, in order to increase the SNR (signal-to-noise ratio) of the cross-correlation of tracer particle image, image enhancement is employed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.221-225
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• 1998

This paper is to present a method for recognizing an image of a tracking object by processing the image from a camera, whose attitude is controlled in inertial space with inertial co-ordinate system. In order to recognize an object, a pseudo-random M-array is attached on the object and it is observed by the camera which is controlled on inertial coordinate basis by inertial stabilization unit. When the attitude of the camera is changed, the observed image of M-array is transformed by use of affine transformation to the image in inertial coordinate system. Taking the cross-correlation function between the affine-transformed image and the original image, we can recognize the object. As parameters of the attitude of the camera, we used the azimuth angle of camera, which is de-fected by gyroscope of an inertial sensor, and elevation an91e of camera which is calculated from the gravitational acceleration detected by servo accelerometer.

• 한국멀티미디어학회논문지
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• 제20권7호
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• pp.979-985
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• 2017

This paper, which covers a fluorescence microscopy image of brain tumor cells, looks at drug reactions by treating different types and concentrations of drugs on a plate of $24{\times}16$ wells. Due to the limitation of the field of view, a well was taken into 9 field images, and each has an overlapping area with its neighboring fields. To analyze more precisely, image stitching is needed. The basic method is finding a similar area using normalized cross-correlation (NCC). The problem is that some overlapping areas may not have any duplicated cells that help to find the matching point. In addition, the cell objects have similar sizes and shapes, which makes distinguishing them difficult. To avoid calculating similarity between blank areas and roughly distinguishing different cells, thresholding is added. The thresholding method classifies background and cell objects based on fixed thresholds and finds the location of the first seen cell. After getting its location, NCC is used to find the best correlation point. The results are compared with a simple boundary stitched image. Our proposed method stitches images that are connected in a grid form without collision, selecting the best correlation point among areas that contain overlapping cells and ones without it.