• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권12호
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• pp.5507-5528
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• 2016

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.539-547
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• 2005

We propose the Bayesian testing for the equality of two independent Inverse Gaussian population means using the fractional Bayesian factors suggested by O' Hagan(1995). As prior distribution for the parameters, we assumed the noninformative priors. In order to investigate the usefulness of the proposed Bayesian testing procedures, the behaviors of the proposed results are examined via real data analysis.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.843-850
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• 2006

In this paper, we consider the Bayesian multiple comparisons problem for K bivariate exponential populations to make inferences on the relationships among the parameters based on observations. And we suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. Also, we give a numerical examples to illustrate our procedure.

• 공학논문집
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• 제2권1호
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• pp.49-56
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• 1997

Emission computed tomography에 있어서 Bayesian방법에 근거한 통계학적 영상 재구성법이 수년간에 걸쳐 중요한 관심사로 대두되어 왔다. 이는 Bayesian 접근 방법을 사용할 경우 영상 재구성 알고리즘에 재구성하고자 하는 영상에 대한 사전정보를 포함시킬 수 있기 때문이었다. 이러한 방법은 경우에 따라 향상된 성능을 보이고 있으나, 막대한 계산시간으로 인해 실제 임상에 적용되기가 매우 어려운 상황이다. 한편, filtered backprojection(FBP)은 알고리즘 자체가 간단하고 계산시간도 매우 단축되므로 대부분의 임상에 널리 적용되고 있다. 본 연구에서는 Bayesian 영상 재구성에서 매우 유용하게 사용되는 spline 모델을 FBP의 고주파 감쇠를 위한 정칙자로 사용함으로써 FBP 영상 재구성에 Bayesian 방법과 유사한 효과를 얻을 수 있음을 보인다.

• 대한전자공학회논문지SP
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• 제42권4호
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• pp.39-48
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• 2005

본 논문에서는 Bayesian 추정법과 신경회로망을 이용한 새로운 결 분할 방법을 제안한다 신경회로망의 입력으로는 다중스케일을 가지는 웨이블릿 계수와 인접한 이웃 웨이블릿 계수들의 문맥정보를 사용하고, 신경회로망의 출력을 사후 확률로 모델링한다. 문맥정보는 HMT(Hidden Markov Tree) 모델을 이용하여 구한다. 제안 방법은 HMT를 이용한 ML(Maximum Likelihood) 분할 보다 더 우수한 결과를 보여준다. 또한 HMT를 이용한 결 분할 방법과 제안 방법을 이용한 결 분할 각각에 HMTseg라고 불리는 다중 스케일 Bayesian 영상 분할 기술을 이용하여 후처리를 행한 결 분할 또한 제안 방법이 우수함을 보여준다.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.561-581
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• 2017

Bayesian statistics can play a key role in the design and analysis of clinical trials and this has been demonstrated for medical device trials. By 1995 Bayesian statistics had been well developed and the revolution in computing powers and Markov chain Monte Carlo development made calculation of posterior distributions within computational reach. The Food and Drug Administration (FDA) initiative of Bayesian statistics in medical device clinical trials, which began almost 20 years ago, is reviewed in detail along with some of the key decisions that were made along the way. Both Bayesian hierarchical modeling using data from previous studies and Bayesian adaptive designs, usually with a non-informative prior, are discussed. The leveraging of prior study data has been accomplished through Bayesian hierarchical modeling. An enormous advantage of Bayesian adaptive designs is achieved when it is accompanied by modeling of the primary endpoint to produce the predictive posterior distribution. Simulations are crucial to providing the operating characteristics of the Bayesian design, especially for a complex adaptive design. The 2010 FDA Bayesian guidance for medical device trials addressed both approaches as well as exchangeability, Type I error, and sample size. Treatment response adaptive randomization using the famous extracorporeal membrane oxygenation example is discussed. An interesting real example of a Bayesian analysis using a failed trial with an interesting subgroup as prior information is presented. The implications of the likelihood principle are considered. A recent exciting area using Bayesian hierarchical modeling has been the pediatric extrapolation using adult data in clinical trials. Historical control information from previous trials is an underused area that lends itself easily to Bayesian methods. The future including recent trends, decision theoretic trials, Bayesian benefit-risk, virtual patients, and the appalling lack of penetration of Bayesian clinical trials in the medical literature are discussed.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1621-1629
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• 2016

This paper studies Bayesian pooling for analysis of categorical data from small areas. Many surveys consist of categorical data collected on a contingency table in each area. Statistical inference for small areas requires considerable care because the subpopulation sample sizes are usually very small. Typically we use the hierarchical Bayesian model for pooling subpopulation data. However, the customary hierarchical Bayesian models may specify more exchangeability than warranted. We, therefore, investigate the effects of pooling in hierarchical Bayesian modeling for the contingency table from small areas. In specific, this paper focuses on the methods of direct or indirect pooling of categorical data collected on a contingency table in each area through Dirichlet priors. We compare the pooling effects of hierarchical Bayesian models by fitting the simulated data. The analysis is carried out using Markov chain Monte Carlo methods.

• 한국멀티미디어학회논문지
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• 제13권6호
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• 한국멀티미디어학회논문지
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• 제4권2호
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• pp.171-181
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• 2001

기존의 베이지만 문서 분류를 위한 단어 군집 방법은 많은 시간과 노력을 요구하며, 단어 간의 의미 관계를 정확하게 반영하지 못하는 문제점이 있다. 본 논문에서는 마이닝 기법으로 구축된 연관 단어 지식 베이스를 기반으로 하는 베이지안 문서 분류 방법을 제안한다. 제안된 베이지안 문서 분류 방법은 문서를 분류하기 전에 훈련 문서를 사용하여 가중치가 부여된 연관 단어 지 식 베이스를 구축한다. 그 다음으로, 베이지안 확률을 이용하는 분류자는 구축된 연관 단어 지식 베이스를 기반으로 문서를 클래스별로 분류한다. 제안된 방법의 성능을 평가하기 위해, 상호 정보 계산에 의한 단어 사전을 이유한 가중치가 부여된 베이지안 문서 분류 방법, 가중치가 부여된 베이지안 분류 방법, 기존의 단순 베이지안 분류 방법과 비교하였다. 그 결과, 연관 단어 지식 베이스에 기반한 가중치가 부여된 베이지안 분류 방법이 상호 정보에 의한 단어 사진을 이용하는 가중치가 부여된 베이지안 분류 방법보다는 0.87%, 가중치가 부여된 베이지안 분류 방법보다는 2.77%, 단순 베이지안 방법보다는 5.97% 높은 성능 차이를 보였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2006년도 추계학술발표대회
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• pp.19-22
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• 2006

In this paper we present a vision based place recognition method which uses Bayesian method with feed back of image retrieval. Both Bayesian method and image retrieval method are based on interest features that are invariant to many image transformations. The interest features are detected using Harris-Laplacian detector and then descriptors are generated from the image patches centered at the features' position in the same manner of SIFT. The Bayesian method contains two stages: learning and recognition. The image retrieval result is fed back to the Bayesian recognition to achieve robust and confidence. The experimental results show the effectiveness of our method.