• 대한원격탐사학회지
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• 제25권5호
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• pp.455-464
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• 2009

Lee(2009)에서 영상 강도를 위해서 lognormal 확률 모형과 영상 texture를 위해서 Markov random field(MRF)에 기반하는 Bayesian 모형을 사용하는 boundary-adaptive despeckling 방법을 제안하였다. 이 방법은 speckle 제거 영상의 최대 사후(maximum a posteriori: MAP) 추정치를 구하기 위해서 Point-Jacobian iteration을 이용한다 인접하고 있는 다른 특성의 지역에 위치한 화소의 값을 사용하는 가능성을 줄이기 위해 Boundary-adaptive algorithm은 경계에 가까울 수록 멀리 떨어진 이웃 화소로부터 정보를 덜 수집하도록 고안된다. 이러한 boundary-adaptive 방법은 전반적으로 simulation 자료를 사용하여 Lee(2009)에서 평가되었고 그리고 제안된 방법의 효험을 증명하였다. 본 연구는 Lee(2009)의 확장 연구로 MAP 추정치를 구하기 반복 algorithm의 계산 효율성을 증가 시키고 noise 제거와 함께 분류를 수행하는 수정 algorithm을 제안한다. Simulation 자료를 사용한 실험을 통해서 boundary-adaption이 분류 오류를 줄여줄 뿐 아니라 더욱 명확한 경계선을 보여준다는 것을 알 수 있다. 또한 영종도 서해안에서 관측된 고해상도 Terra-SAR data에 적용한 결과는 boundary-adaption은 SAR 활용에서 분석의 정확성을 개선 시킬 수 있다는 것을 암시한다.

• Communications for Statistical Applications and Methods
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• 제20권3호
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• pp.225-233
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• 2013

The linear support vector machine(SVM) is motivated by the maximal margin separating hyperplane and is a popular tool for binary classification tasks. Many studies exist on the consistency properties of SVM; however, it is unknown whether the linear SVM is consistent for estimating the optimal classification boundary even in the simple case of two Gaussian classes with a common covariance, where the optimal classification boundary is linear. In this paper we show that the linear SVM can be inconsistent in the univariate Gaussian classification problem with a common variance, even when the best tuning parameter is used.

• 농업과학연구
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• 제47권2호
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• pp.205-217
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• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

• ETRI Journal
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• 제38권1호
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• pp.195-205
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• 2016

This paper presents a novel follicular unit (FU) classification method based on an angle variation of a boundary vector according to the number of hairs in several FU images. The recently developed robotic FU harvest system, ARTAS, classifies through digital imaging the FU type based on the number of hairs with defects in the contour and outline profile of the FU of interest. However, this method has a drawback in that the FU classification is inaccurate because it causes unintended defects in the outline profile of the FU. To overcome this drawback, the proposed method classifies the FU's type by the number of variation points that are calculated using an angle variation a boundary vector. The experimental results show that the proposed method is robust and accurate for various FU shapes, compared to the contour-outline profile FU classification method of the ARTAS system.

• 대한전자공학회논문지
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• 제27권1호
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• pp.120-129
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• 1990

In this paper, 3-dimensional object segmentation and classification are proposed. Planar object is segmented surface using jump boundary and internal boundary. Curved object is segmented surfaces by maximin clustering method. Segmented surfaces are classified by depth trends and angle measurement of normal vectors. Classified surfaces are merged according to adjacent surfaces and compared to Guassian curvature and mean curvature method. The proposed methods have been successfully applied to the synthetic range images and shows good classification.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.917-922
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• 1990

Pattern classification is an essential step in automatic robotic assembly which joins together finite number of seperated industrial parts. In this paper, a fast and systematic algorithm for classifying occlusion-free objects is proposed, using the notion of incremental circle transform which describes the boundary contour of an object as a parametric vector function of incremental elements. With similarity transform and line integral, normalized determinant curve of an object classifies each object, independent of position, orientation, scaling of an object and cyclic shift of the stating point for the boundary description.

• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.89-97
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• 2021

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

• 대한원격탐사학회지
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• 제20권2호
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• pp.139-152
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• 2004

본 연구에서는 수정이방성복원 후 지역확장분할 영상분류의 분류오류를 Markov Random Field(MRF) 기반 분류자를 사용하여 개선시킬 것을 제안하고 있다. 제안 접근법은 지역확장분할 분류에 의해 생성된 결과에서 분류오류의 발생 가능성이 높은 경계지역을 정의하고 경계지역내의 화소들에 대해 재분류를 수행하여 수정하는 것이다. 재분류를 위한 MRF 기반 분류자는 지역확장분할 분류에 의해 추정된 클래스 수와 클래스 특성 값을 기반으로 하여 분류를 수행하는 반복적인 기법이다. 모의자료에 대한 실험은 제안 기법이 분류 정확성을 향상시킴을 보여주었다 그러나 실제적으로 많은 탐사지역의 피복형태는 매우 복잡한 구조를 갖고 있으므로 일반적 MRF 기반 기법의 사용은 원격탐사 영상의 정확한 분석을 이끌어 내지 못할 수 있으므로 본 연구는 다중 분류자를 사용하는 다단계 경계지역 수정기법을 제안한다. 한반도의 실제 원격탐사 영상자료에 대한 적용결과는 다단계 기법의 효과성을 잘 보여주고 있다. 다단계 반복적 경계지역 내 분류수정은 분석지역에 존재하는 자세한 구조를 보존하는 한편 지역적 명확한 구분의 분류결과를 생성한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.907-910
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• 1998

In this paper, we present a comparative performance analysis of several blocking artifact reduction algorithms. For the performance analysis, we propose a block boundary region classification algorithm which classifies each horizontal and vertical block boundary into four regions using brightness change near the block boundary. The PSNR performance of each algorithm is compared. The MSE according to each block boundary region is also compared. Experimental results show that the wavelet transform based blocking artifact reduction algorithms have better performance over the other methods.

• Journal of applied mathematics & informatics
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• 제8권1호
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• pp.129-136
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• 2001

We introduce and discuss a new numerical method for solving system of second order boundary value problems, where the solution is required to satisfy some extra continuity conditions on the subintervals in addition to the usual boundary conditions. We show that the present method gives approximations which are better than that produced by other collocation, finite difference and spline methods. Numerical example is presented to illustrate the applicability of the new method. AMS Mathematics Subject Classification : 65L12, 49J40.