• 대한전기학회논문지:시스템및제어부문D
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• 제52권3호
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• pp.148-155
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• 2003

In this paper, a nonlinear anisotropic filtering method without the loss of important information happened due to the repeated filtering in magnetic resonance images is proposed. First of all original images are divided into four regions, e.g., SPR(Strong Plain Region), EPR(Easy Plain Region), SER(Strong Edge Region), and EER(Easy Edge Region). An optimal template among multiple templates is selected, then the nonlinear anisotropic filtering based on the template is applied in pixel by pixel basis. In the proposed algorithm, filtering strength of EER containing important information is adjusted very weak and filtering strength for remaining regions is also adjusted according to the degree of the importance. In spite of repeated filtering, resulting images by the proposed method could still preserve anatomy information of original images without any degradation. Compared to the existing nonlinear anisotropic filtering, the proposed filtering method with multiple templates provides higher reliability for filtered images.

• 로봇학회논문지
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• 제3권3호
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• pp.176-185
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• 2008

In this paper, new filtering method for sensor registration is provided to estimate and correct error of registration parameters in multiple sensor environments. Sensor registration is based on filtering method to estimate registration parameters in multiple sensor environments. Accuracy of sensor registration can increase performance of data fusion method selected. Due to various error sources, the sensor registration has registration errors recognized as multiple objects even though multiple sensors are tracking one object. In order to estimate the error parameter, new nonlinear information filtering method is developed using minimum mean square error estimation. Instead of linearization of nonlinear function like an extended Kalman filter, information estimation through unscented prediction is used. The proposed method enables to reduce estimation error without a computation of the Jacobian matrix in case that measurement dimension is large. A computer simulation is carried out to evaluate the proposed filtering method with an extended Kalman filter.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.105-110
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference. data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values.. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.441-450
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• 2003

Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.236-237
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• 2019

본 논문에서는 웨이블릿 기반의 영상의 노이즈 제거 알고리즘을 제안한다. 기존 Mallat Tree 방식의 웨이블릿 변환을 응용한다. 먼저, 다중 필터링 방법을 제시한다. 웨이블릿 영역에서 각 부대역의 에너지를 고려하여 웨이블릿 변환의 에너지 집중 특성을 극대화 시킨다. 노이즈 영상에 제안한 다중 필터링을 적용한다. 일반 영상에서 나올 수 없는 에너지 부대역을 찾고 이를 제거하여 노이즈를 제거한다.

• 전자공학회논문지SC
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• 제46권5호
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• pp.22-28
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• 2009

본 논문에서는 다중 상태 시변 시간지연을 가지는 연속시간 특이시스템의 $H_{\infty}$ 필터링 문제를 다룬다. 제안하는 필터의 목적은 필터링 오차 특이시스템(filtering error singular system)이 정규성, 임펄스 프리, 점근적 안정성 및 $H_{\infty}$ 노옴 유계(bound)를 만족하는 선형 필터를 설계하는 것이다. 먼저, 다중 상태 시변 시간지연을 가지는 특이시스템에 대한 새로운 지연종속 유계실수정리(bounded real lemma)를 자승 요소를 기초로 하는 유한 합 부등식(finite sum inequality)을 이용하여 제안하고, 이로부터 $H_{\infty}$ 필터가 존재할 조건과 필터의 설계기법을 최적화가 가능한 선형행렬부등식(linear matrix inequality)으로 제시한다. 마지막으로 예제를 통하여 제안한 필터 설계 알고리듬의 타당성을 확인한다.

• Investigative Magnetic Resonance Imaging
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• 제4권2호
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• pp.120-127
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• 2000

최근에 해상도의 손실없이 신호대잡음비를 개선시킬 수 있는 적응 템플릿 필터링이 제안되었다. 적응 템플릿 필터링은 다중 템플릿들 중에서 현재 복셀의 주변 구조와 가장 잘 매칭이 되는 템플릿을 선택하여 적응필터 링을 적용하는 방법이다. 적응 템플릿 필터링을 자기공명영상에 적용할때 기존의 필터링 방법들에 비하여 향상된 결과를 얻을 수 있으나, $T_1$ 영상과 같이 비교적 작은 동적 범위를 가진 영상에서는 에지에서 계단모양의 artifact가 발견되곤 한다. 이것은 자기 공명영상에서 복셀의 부분적인 볼륨 효과에 기인하는 것으로 여러 조직의 성분을 포함하고 있는 경계면의 복셀들에 적응 템플릿 필터링이 적용될 경우 다중성분을 가진 복셀들의 그레이레벨이 인접한 단일성분의 그레이레벨 값에 가까워져 에지가 강조되기 때문이다 본 논문에서는 다중 성분을 갖는 복셀들을 선별하여 이들에 대해서는 가장 큰 크기의 템플릿을 할당함으로써 artifact를 제거하는 방법을 제안하였다. 제안한 방법을 $T_1$ 자기공명영상과 팬텀 영상에 적용한 결과 에지 artifact가 사라지는 것을 확인할 수 있었으며, 최대 신호대잡음비 면에서도 향상된 결과를 얻을 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.478-481
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• 1996

Tracking a target of versatile maneuver recently demands a stable adaptation of tracker, and the multiple model techniques are being developed because of its ability to produce useful information of target maneuver. This paper presents the way to apply the multiple model method in a moving-target and moving-platform scenario, and the estimation and prediction results better than those of single Kalman filter.

• 지능정보연구
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• 제9권2호
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• pp.51-63
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1157-1165
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• 2017

2-D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2-D complex Gabor filter bank consisting of the 2-D complex Gabor filtering outputs at multiple orientations and frequencies. Although several approaches for fast 2-D complex Gabor filtering have been proposed, they primarily focus on reducing the runtime of performing the 2-D complex Gabor filtering once at specific orientation and frequency. To obtain the 2-D complex Gabor filter bank output, existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2-D complex Gabor filter bank by reducing the computational redundancy that arises when performing the Gabor filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor basis kernels to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the 2-D complex Gabor filter bank by reusing intermediate results of the 2-D complex Gabor filtering computed at a specific orientation. Experimental results demonstrate that our method runs faster than state-of-the-arts methods for fast 2-D complex Gabor filtering, while maintaining similar filtering quality.