• Current Optics and Photonics
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• 제4권4호
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• pp.317-325
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• 2020

Accurate estimation of spatial frequencies and phases for illumination patterns are essential to reconstructing super-resolution images in structured illumination microscopy (SIM). In this manuscript, we propose the improved component cross-correlation (ICC) algorithm, which is based on optimization of the cross-correlation values of the overlapping information between various spectral components. Compared to other algorithms for spatial-frequency and phase determination, the results calculated by the ICC algorithm are more accurate when the modulation depths of the illumination patterns are low. Moreover, the ICC algorithm is able to calculate the spatial frequencies and phases simultaneously. Simulation results indicate that even if the modulation depth is lower than 0.1, the ICC algorithm still estimates the parameters precisely; the images reconstructed by the ICC algorithm are much clearer than those reconstructed by other algorithms. In experiments, our home-built SIM system was used to image bovine pulmonary artery endothelial (BPAE) cells. Drawing support from the ICC algorithm, super-resolution images were reconstructed without artifacts.

• 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.

• Industrial Engineering and Management Systems
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• 제1권1호
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• pp.79-86
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• 2002

This paper proposes a new algorithm for estimating ARMA model parameters. In estimating ARMA model parameters, several methods such as generalized least square method, instrumental variable method have been developed. Among these methods, the utilization of a bootstrap type algorithm is known as one of the effective approach for the estimation, but there are cases that it does not converge. Hence, in this paper, making use of a cross correlation function and utilizing the relation of structural a priori knowledge, a new bootstrap algorithm is developed. By introducing theoretical relations, it became possible to remove terms, which is liable to include much noise. Therefore, this leads to robust parameter estimation. It is shown by numerical examples that using this algorithm, all simulation cases converge while only half cases succeeded with the previous one. As for the calculation time, judging from the fact that we got converged solutions, our proposed method is said to be superior as a whole.

• 한국가시화정보학회지
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• 제3권2호
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• pp.71-78
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• 2005

Uncertainty introduced by a cross-correlation algorithm based on FFT has been investigated using PIV standard images. The standard images were generated by the Monte Carlo simulation method. Both bias and random errors from the velocity vector have been analyzed with regard to the particle diameter, displacement, and the number of particles. The uncertainty of velocity is evaluated based upon the IS0/IEC standard. As a result, a total error of $0.26\%$ is included in the PIV cross-correlation algorithm. In addition, the uncertainty budget is presented, where the effect of the above three variables is examined. According to the budget, the variation of the number of particles within the interrogation window mainly contributes to the combined standard uncertainty of the real measured velocity field when excluding the effect of errors by the experiments itself. Finally, the expanded uncertainty is found to be about $12\%$ at the $95\%$ confidence level.

• 대한전자공학회논문지SP
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• 제40권4호
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• pp.248-255
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• 2003

본 논문에서는 상호상관함수를 이용한 지문인식 기법을 제시한다. 본 기법은 특징추출, 지문정렬, 지문매칭으로 구성되는데, 이중에서 본 논문은 새로운 지문정렬 기법을 제안한다. 본 지문정렬 기법에서는 특징점의 융선방향 각도에 대해 상호상관함수를 적용하여 두 지문사이의 회전각도를 구하고, 그후 회전된 지문으로부터 두 지문간의 이동변위를 구하여 지문을 정렬한다. 본 정렬기법은 과도한 탐색에 의존하지 않고 두 지문의 회전각도와 이동변위를 구할 수 있다 제시된 기법으로 지문인식 실험을 한 결과 2.086%의 타인수락오류율(FAR)에 대해 17.299%의 본인거부오류율(FRR)을 얻을 수 있었다.

• 센서학회지
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• 제9권4호
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• pp.316-326
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• 2000

본 논문에서는 백색광 간섭현상 (white light interferometry)을 위한 신호처리 알고리즘을 제안한다. 제안하는 알고리즘으로 간섭기의 광경로 절대길이 (absolute optical path length)를 정확하게 측정할 수 있다. 그리고 제안하는 알고리즘은 간섭 무늬 (fringe scan)의 상호 상관관계 함수 (cross-correlation function)와 가설 검증을 사용한다. 가설 검증은 간섭 무늬의 상호 상관관계 함수가 대칭이 되는 봉우리를 영차 간섭 봉우리 (zero order fringe peak) 후보자로 선정함으로써 영차 간섭 봉우리를 오판할 확률을 줄인다. 산탄잡음(shot noise)이 제안된 알고리즘의 성능에 미치는 영향을 컴퓨터 모의 실험을 통하여 조사하였다. 모의 실험결과와 보외법 (extrapolation)을 사용하여 신호대산탄잡음비 (signal-to-shot noise ratio)가 31 dB 보다 클 때의 알고리즘의 성능을 예측하였다. 간섭 봉우리의 세 가지 매개변수 변화 (신호대산탄잡음비, 간섭 스캔 샘플링율 광원의 가간섭성 길이)에 따른 영차 간섭 봉우리 추정 오차를 계산하였다. 모의 실험 결과를 통하여 제안한 알고리즘이 영차 간섭 무늬 봉우리를 정확하게 판별할 수 있음을 보여주었다. 제안하는 신호처리 알고리즘은 소프트웨어적인 기법으로서 경제적이고 속도가 빠르며 간단한 알고리즘이다.

• 산업기술연구
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• 제29권B호
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• pp.17-23
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• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.

• 대한의용생체공학회:의공학회지
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• 제23권2호
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• pp.101-107
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• 2002

이 논문에서는 자기공명심장영상에서 내벽과 외벽의 추출을 위한 반자동 분할 알고리즘을 제안하였다. 이 알고리즘은 Generalized gradient vector flow snake와 초기 윤곽선 예측 과정을 기반으로 한다. 특히 이 알고리즘은 내벽과 외벽의 공간적인 특설을 이용하며 Cross profile correlation matching (CPCM)을 사용한다. 현재 공간에서의 이전 시간에 관계된 영상과 현재 시간에서의 공간에 관계된 영상을 사용하여 초기 윤곽선 예측을 더욱 효과적으로 수행하였다. Multislice와 multiphase의 Siemens와 GE. Medinus 자기공명심장영상을 사용하여 실험하였고 많은 영상들에 대해 충분히 만족할만한 결과를 얻었다. 그리고 분할한 결과로 quantitative analysis를 수행하였고 시각적으로 보여주었다. 개발된 소프트웨어는 Visual C++을 사용하여 windows 환경의 응용프로그램으로 개발되었다.

• 한국가시화정보학회:학술대회논문집
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• 한국가시화정보학회 2001년도 Proceedings of 2001 Korea-Japan Joint Seminar on Particle Image Velocimetry
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• pp.75-85
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• 2001

The hierarchic recursive local-correlation PIV algorithm with CBC(correlation based correction) method was developed to increase the spatial resolution of PIV results and to reduce error vectors. This new algorithm was applied to the single-frame and double-frame cross-correlation PIV techniques. In order to evaluate its performance, the recursive algorithm was tested using synthetic images, PIV standard images from Visualization Society of Japan, real flows including ventilation flow inside a vehicle passenger compartment and wake behind a circular cylinder with rib let surface. As a result, most spurious vectors were suppressed by employing CBC method. In addition, the hierarchical recursive correlation algorithm improved largely the sub-pixel accuracy of PIV results by decreasing the interrogation window size, increasing spatial resolution significantly.

• 대한조선학회논문집
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• 제44권6호
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• pp.579-589
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• 2007

Contrary to the conventional single-point measuring devices such as LDV, pitot-tube, hot-wire, etc., it would be possible to measure instantaneously 3-D flow fields with a stereoscopic PIV system. In this paper, we present an analysis algorithm for a stereoscopic PIV system using the normalized cross-correlation (NCC) and a 3-D calibration based reconstruction method. The evaluation method based on NCC is one of the most accurate correlation-based methods. We validated the developed algorithm through a benchmarking comparison with 3-D artificial SPIV images and calibration target images.