• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.643-654
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• 2000

In this paper, we review some class of t-norms on which fuzzy arithmetic operations preserve the shapes of fuzzy numbers and the Hausdorff-distance between fuzzy numbers as the measure of distance between fuzzy numbers. And we suggest the least Hausdorff-distance square method for fuzzy linear regression model using shape preserving fuzzy arithmetic operations.

• 한국지능시스템학회논문지
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• 제12권5호
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• pp.467-472
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• 2002

본 논문에서, 영상에서 임펄스 잡음을 효과적으로 제거하고, 연산 속도를 개선하기 위해 Fuzzy Cellular Neural Network(FCNN)구조에 Hausdorff distance(HD)를 적용한 $\alpha$-Least Trimmed Square HD($\alpha$-LTSHD) 기반 FCNN 구조를 제안한다. FCNN는 Cellular Neural Network(CNN) 구조에 퍼지 이론을 적용한 것이고, HD는 특징 대상의 대응 없이 이진 영상의 두 픽셀 집합 사이의 거리를 구하는 척도로 물체의 정합에 널리 사용한다. 성능 평가를 위해, 제안된 방법을 MSE와 SNR을 이용하여 기존 FCNN, Opening-Closing(OC) 그리고 LTSHD 연산자를 적용한 FCNN과 비교 분석하였다. 그 결과, 본 논문에서 제안된 망(network) 구조의 성능이 다른 필터보다 임펄스 잡음 제거에 우수함을 확인하였다.

• 대한기계학회논문집A
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• 제25권5호
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• pp.841-849
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• 2001

The pixel information of the object was obtained sequentially and pixels were clustered to a label by the line labeling method. Feature points were determined by finding the slope for edge pixels after selecting the fixed number of edge pixels. The slope was estimated by the least square method to reduce the detection error. Once a matching point was determined by comparing the feature information of the object and the pattern, the parameters for translation, scaling and rotation were obtained by selecting the longer line of the two which passed through the matching point from left and right sides. Finally, modified Hausdorff Distance has been used to identify the similarity between the object and the given pattern. The multi-label method was developed for recognizing the patterns with more than one label, which performs the modified Hausdorff Distance twice. Experiments have been performed to verify the performance of the proposed algorithm and method for simple target image, complex target image, simple pattern, and complex pattern as well as the partially hidden object. It was proved via experiments that the proposed image matching algorithm for recognizing the object had a good performance of matching.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.50-53
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• 2000

It is effectively removed noise in the image using FCNN(Fuzzy Cellular Neural Network) applying fuzzy theory to CNN(Cellular Neural Network) structure and HD(Hausdorff Distance) commonly used measures for object matching. HD calculates the distance between two point set of pixels in two-dimensional binary images without establishing correspondence. Also, this method is proposed in order to improve the operation speed. In this paper, $\alpha$-LTSHD(Least Trimmed Square HD) operator applying $\alpha$-Trimmed to LTSHD, one field of HD, is applied to FCNN structure, and it is proposed as the modified method in order to remove noise in the image. Also, it is made a comparison with the other filters by using MSE and SNR after removing noise using the FCNNS which are applied $\alpha$-LTSHD operator through the computer simulation. In a result, FCNN performance which is applied the proposed $\alpha$-LTSHD demonstrated the superiority to the other filters in the noise removal.