• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제18권3호
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• pp.225-244
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• 2014

In this work, we discuss segmentation algorithms based on the level set method that incorporates shape prior knowledge. Fundamental segmentation models fail to segment desirable objects from a background when the objects are occluded by others or missing parts of their whole. To overcome these difficulties, we incorporate shape prior knowledge into a new segmentation energy that, uses global and local image information to construct the energy functional. This method improves upon other methods found in the literature and segments images with intensity inhomogeneity, even when images have missing or misleading information due to occlusions, noise, or low-contrast. We consider the case when the shape prior is placed exactly at the locations of the desired objects and the case when the shape prior is placed at arbitrary locations. We test our methods on various images and compare them to other existing methods. Experimental results show that our methods are not only accurate and computationally efficient, but faster than existing methods as well.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.24.3-24
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• 2001

In cases where strong a priori knowledge about the object being analyzed is available, it can be embedded into the formulation of the snake model. When prior knowledge of shape is available for a specific application, information concerning the shape of the desired objects can be incorporated into the formulation of the snake model as an active contour model. In this paper we show Five points algorithm can be applied to design invariant energy.

• 한국지구과학회지
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• 제32권1호
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• pp.140-151
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• 2011

본 연구는 예비 지구과학교사들이 개발한 교정 교수학습자료의 문제점과 개발 과정에 영향을 주는 요소를 찾는 것이다. 이를 위해 전라북도에 소재하는 사범대학 과학교육학부(지구과학전공)에 재학 중인 54명(남자 18명, 여자 36명)으로부터 258개의 교정 교수학습자료를 수집하였다. 이를 분석한 결과 교정 교수학습자료는 개발하는 절차와 예비 지구과학교사들의 선행 지식에 의해 영향을 받고 있었다. 교정 탐색 우선형은 이미 형성된 선행 지식 범위 내에서 자료를 개발하기 때문에 이들이 개발한 자료는 교육과정의 특정 영역에 편중되어 있다. 이에 비해 개념 탐색 우선형은 대부분의 예비 지구과학교사들이 접근하지 못한 영역의 자료를 개발하였다. 올바른 선행 지식을 형성하지 못한 예비 지구 과학교사들은 잘못된 소재를 선택할 뿐 아니라 개념 연결에서도 오류를 범하고 있다. 또한, 대부분의 예비 지구과학교사들이 지질현상에 대한 과학개념을 형성하고 있다고 하더라도 생성과정을 무시하고 소재의 형태만을 주로 고려하고 있다. 그러므로 지질조사 및 고체지구과학 분야에 대한 충분한 학습이 가능하도록 지구과학교사 양성 교육과정에 충실히 반영해야 하며, 이를 바탕으로 예비 지구과학교사들의 지질학적 내용지식, 야외 교수학습 전략, 자료개발 절차 등이 하나의 통합적인 개념으로 형성될 수 있도록 지도할 필요가 있다.

• 한국멀티미디어학회논문지
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• 제20권9호
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• pp.1491-1501
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• 2017

In this paper, we propose a method to measure respiration rate by dividing the respiration related region in depth image using level set method. In the conventional method, the respiration related region was separated using the pre-defined region designated by the user. We separate the respiration related region using level set method combining shape prior knowledge. Median filter and clipping are performed as a preprocessing method for noise reduction in the depth image. As a feasibility test, respiration activity was recorded using depth camera in various environments with arm movements or body movements during breathing. Respiration activity was also measured simultaneously using a chest belt to verify the accuracy of calculated respiration rate. Experimental results show that our proposed method shows good performance for respiration rate estimation in various situation compared with the conventional method.

• 한국멀티미디어학회논문지
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• 제17권1호
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• pp.1-7
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• 2014

Hippocampus is an important part of brain which is related with early memory storage and spatial navigation. By observing the anatomy of hippocampus, some brain diseases effecting human memory (e.g. Alzheimer, schizophrenia, etc.) can be diagnosed and predicted earlier. The diagnosis process is highly related with hippocampus segmentation. In this paper, hippocampus segmentation using Active Shape Model, which not only works based on image intensity, but also by using prior knowledge of hippocampus shape and intensity from the training images, is proposed. The results show that ASM is applicable in segmenting hippocampus from whole brain MR image. It also shows that adding more images in the training set results in better accuracy of hippocampus segmentation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.570-574
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• 2003

In most boundary estimation algorithms estimation in EIT (Electrical Impedance Tomography), anomaly boundaries can be expressed with Fourier series and the unknown coefficients are estimated with proper inverse algorithms. Furthermore, the number of anomalies is assumed to be available a priori. The prior knowledge on the number of anomalies may be unavailable in some cases, and we need to determine the number of anomalies with other methods. This paper presents an algorithm for the boundary estimation in EIT (Electrical Impedance Tomography) using the prior information from the conventional Newton-Raphson method. Although Newton-Raphson method generates so poor spatial resolution that the anomaly boundaries are hardly reconstructed, even after a few iterations it can give general feature of the object to be imaged such as the number of anomalies, their sizes and locations, as long as the anomalies are big enough. Some numerical experiments indicate that the Newton-Raphson method can be used as a good predictor of the unknown boundaries and the proposed boundary discrimination algorithm has a good performance.

• Nuclear Engineering and Technology
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• 제49권4호
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• pp.692-699
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• 2017

The determination of the shape and rising velocity of gas bubbles in a liquid pool is of great importance in analyzing the radioactive aerosol emissions from nuclear power plant accidents in terms of the fission product release rate and the pool scrubbing efficiency of radioactive aerosols. This article suggests a simple parameterization for the gas bubble rising velocity as a function of the volume-equivalent bubble diameter; this parameterization does not require prior knowledge of bubble shape. This is more convenient than previously suggested parameterizations because it is given as a single explicit formula. It is also shown that a bubble shape diagram, which is very similar to the Grace's diagram, can be easily generated using the parameterization suggested in this article. Furthermore, the boundaries among the three bubble shape regimes in the $E_o-R_e$ plane and the condition for the bypass of the spheroidal regime can be delineated directly from the parameterization formula. Therefore, the parameterization suggested in this article appears to be useful not only in easily determining the bubble rising velocity (e.g., in postulated severe accident analysis codes) but also in understanding the trend of bubble shape change due to bubble growth.

• ETRI Journal
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• 제33권4호
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• pp.600-610
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• 2011

This paper presents a novel approach to face detection by localizing faces as the goal-specific saliencies in a scene, using the framework of selective visual attention of a human with a particular goal in mind. The proposed approach aims at achieving human-like robustness as well as efficiency in face detection under large scene variations. The key is to establish how the specific knowledge relevant to the goal interacts with the bottom-up process of external visual stimuli for saliency detection. We propose a direct incorporation of the goal-related knowledge into the specification and/or modification of the internal process of a general bottom-up saliency detection framework. More specifically, prior knowledge of the human face, such as its size, skin color, and shape, is directly set to the window size and color signature for computing the center of difference, as well as to modify the importance weight, as a means of transforming into a goal-specific saliency detection. The experimental evaluation shows that the proposed method reaches a detection rate of 93.4% with a false positive rate of 7.1%, indicating the robustness against a wide variation of scale and rotation.

• 대한전자공학회논문지SP
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• 제47권6호
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• pp.75-81
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• 2010

연골 영역화는 골관절염의 진단이나 치료를 위해 중요하지만, 모양이 얇고 의료영상 내에서 주변 조직과의 명암 차이가 크지 않기 때문에 현재까지 전문가가 많은 시간과 노력을 들여 수동으로 하고 있다. 이에 따라 본 논문에서는 3차원 자기공명(Magnetic Resonance : MR)영상 내에서 연골을 자동으로 영역화하는 기법을 제안한다. 제안하는 기법은 전문가에 의해 수동으로 영역화된 소수의 의료영상을 학습 데이터베이스로 하여 우선 연골을 지역적인 부분(local patch)들로 분할하여 부분별로 영역화한 후, 부분별 결과들을 취합하고 정제하는 과정으로 이루어진다. 연골 영역화를 위해 먼저 위치와 밝기 값의 외관정보 (appearance)를 이용하여 뼈와 연골의 경계(bone-cartilage interface)를 추출해내고, 이 경계를 기준으로 하여 연골이 포함되는 주변 영역을 일정한 크기의 패치로 분할한다. 다음, 분할된 패치들의 정보를 이용해, 패치마다 형상 사전지식(shape prior)과 외관 사전지식(appearance prior)을 얻어내고 두 사전지식 간의 비율을 적응적으로 결정한다. 이후 패치마다 사전지식 정보를 통해 에너지를 정의하고, 그래프 컷(Graph Cut) 기법을 통해 이 에너지를 최소화하는 최적의 영역화 결과를 도출한다. 마지막으로 지역적으로 얻어진 영역화 결과들을 모양 사전지식으로 하여 전체적인 연골에 대해 전역적 개선 과정을 수행한다. 실험 결과를 통해 제안하는 자동 영역화 기법으로 임상적으로 유용한 영역화 결과를 얻을 수 있음을 제시한다.

• 디지털산업정보학회논문지
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• 제10권2호
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• pp.29-36
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• 2014

Finite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, proposes the Gompertz distribution reliability model, which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on mean square error (MSE) and coefficient of determination$(R^2)$, for the sake of efficient model, was employed. Analysis of failure using real data set for the sake of proposing fixed shape parameter of the Gompertz distribution was employed. This analysis of failure data compared with the Gompertz distribution model of shape parameter. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the proposed Gompertz model is more efficient in terms of reliability in this area. Thus, Gompertz model can also be used as an alternative model. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can was helped.