• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.295-298
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• 1995

We performed a research to improve the performance of active bar model which is used in tracking algorithm. Active bar model is a simplified model of snake model. If we used the sctive bar model, the numerical procedure for real time tracking problem can be carried out faster than snake model. However the demerit of active bar algorithms is that we can't used the provious image data because each time it has to reconstruct the active bar. In this paper we proposed advanced algorithm for active bar model. The proposed model can improve tracking abilities by preserving the active bar during the process and changing the energy functional.

• 한국컴퓨터정보학회논문지
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• 제4권4호
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• pp.77-85
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• 1999

본 논문은 Kass가 제안한 수식을 수정했다. 첫 번째로, 물체의 내부나 외부에 초기 값을 설정할 수 있도록 하였으며, 물체의 윤곽선을 보다 빨리 쉽게 추출할 수 있고 에지가 강한 곳으로 움츠려드는 것을 개선하였다. 두 번째로 내부에너지 수식을 개선하였고, $3{times}3$ 행렬을 사용하였다. 세 번째로 에너지 항을 부과하여 한 점에 모이는 현상을 처리했고, 임계값을 사용하여 윤곽선을 정확하게 추출하도록 알고리즘을 수정하였다. 알고리즘을 수정한 결과, 초기 값을 쉽게 설정했고 높은 곡률의 물체도 추출했다. 피드백시스템을 사용하여 처리속도를 개선했다.

• 재활복지공학회논문지
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• 제10권2호
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• pp.141-146
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• 2016

본 논문에서는 설진시스템에서 혀 영역의 윤곽선을 정확하게 검출하기 위해 영역제한 마스크 연산과 능동 윤곽선 모델을 적용한다. 혀의 특징을 정확하게 분석하기 위하여 먼저, 혀 영역이 검출되어야 한다. 그러므로 혀 영역의 에지를 검출하기 위한 효율적인 분할 방법은 매우 중요하다. 20~30대 학생 30명으로 구성된 혀 영상 DB로 실험하였다. 실제 혀 영상에서의 실험은 좋은 결과를 보였다. 실험 결과, 제안된 방법이 마스크 연산을 사용하지 않는 방법에 비해 더 정확하게 혀 영역의 윤곽선을 추출하는 것을 확인할 수 있었다.

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.827-833
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• 2015

In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

• 전기학회논문지
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• 제67권12호
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• pp.1678-1684
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• 2018

Dental caries is one of the most popular oral disease. The aim of automatic dental cavity detection system is helping dentist to make accurate diagnosis. It is very important to separate cavity from the teeth in the detection system. In this paper, We compared two active contour algorithms, Snake and DRLSE(Distance Regularized Level Set Evolution). To improve performance, image is selected ROI(region of interest), then applied bilateral filter, Canny edge. In order to evaluate the algorithms, we applied to 7 tooth phantoms from incisor to molar. Each teeth contains two cavities of different shape. As a result, Snake is faster than DRLSE, but Snake has limitation to compute topology of objects. DRLSE is slower but those of performance is better.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.57.4-57
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• 2002

$\textbullet$ Image segmentation is an essential technique of image analysis. In spite of the traditional issues in contour initialization and boundary concavities, active contour models(snakes) are popular and known as successful methods for segmentation. $\textbullet$ We could find in experiment that snake using Gaussian External Force is fast in time but low in accuracy and snake using Gradient Vector Flow by Chenyang Xu and Jerry L. Prince is high in accuracy but slow in time. $\textbullet$ In this paper, we presented a new active contour model, GGF snake, for segmentation of endoscopic image. Proposed GGF snake made up for the defects of the traditional snakes in contour initialization and boundary...

• 대한자기공명의과학회:학술대회논문집
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• 대한자기공명의과학회 2002년도 제7차 학술대회 초록집
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• pp.126-126
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• 2002

Purpose： For quantitative analysis of the cardiac diseases, it is necessary to segment the left-ventricle(LV) in MR cardiac images. Snake or active contour model has been used to segment LV boundary. In using these models, however, the contour of the LV may not converge to the desirable one because the contour may fall into local minimum value due to image artifact in inner region of the LV Therefore, in this paper, we propose the new preprocessing method using K-means clustering and merging algorithms that can improve the performance of the active contour model.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.753-762
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• 2006

The Snakes and GVF used to find object edges dynamically have assigned their initial contour arbitrarily. If the initial contours are located in the neighboring regions of object edges, Snakes and GVF can be close to the true boundary. If not, these will likely to converge to the wrong result. Therefore, this paper proposes a new initialization of Snakes and GVF using convex hull approximation, which initializes the vertex of Snakes and GVF as a convex polygonal contour near object edges. In simulation result, we show that the proposed algorithm has a faster convergence to object edges than the existing methods. Our algorithm also has the advantage of extracting whole edges in real images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2839-2852
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• 2013

Context-awareness is an essential part of ubiquitous computing, and over the past decade video based activity recognition (VAR) has emerged as an important component to identify user's context for automatic service delivery in context-aware applications. The accuracy of VAR significantly depends on the performance of the employed human body segmentation algorithm. Previous human body segmentation algorithms often engage modeling of the human body that normally requires bulky amount of training data and cannot competently handle changes over time. Recently, active contours have emerged as a successful segmentation technique in still images. In this paper, an active contour model with the integration of Chan Vese (CV) energy and Bhattacharya distance functions are adapted for automatic human body segmentation using depth cameras for VAR. The proposed technique not only outperforms existing segmentation methods in normal scenarios but it is also more robust to noise. Moreover, it is unsupervised, i.e., no prior human body model is needed. The performance of the proposed segmentation technique is compared against conventional CV Active Contour (AC) model using a depth-camera and obtained much better performance over it.

• 한국정보통신학회논문지
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• 제10권10호
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• pp.1891-1896
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• 2006

이 논문에서는 능동형태 모델을 개선하여 입술의 형태를 효과적으로 추출하는 방법을 제안하였다. 입술의 형태변형은 능동형태 모델에 기반을 둔 통계적 형태 변형 모델을 사용하여 표현하였다. 능동형태 모델에서 각 점은 지엽적인 정보인 프로파일을 기반으로 독립적으로 이동하기 때문에 많은 오류가 발생할 수 있다. 전역적인 정보를 사용하기 위하여 이 논문에서는 능동윤곽선 모델에서 사용하는 것과 유사한 에너지 함수를 정의하고 전체 에너지가 최소화되는 위치로 점들이 이동하게 하였다. Tulip 1 데이터 베이스에 있는 입술 영상을 대상으로 실험한 결과, 제안한 방법이 기존 방법보다 실제 형태에 가깝게 입술을 추출하였다.