• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.457-467
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• 2000

In this paper, we propose the method to extract the anatomical objects in medical images using active contour models and genetic algorithms. The performance of active contour models is mostly decided by the optimization of active contour model's energy. So, we propose to use genetic algorithms to optimize the energy of active contour models. We experimented our proposed method on the femoral head medical images and proved that our method provides very acceptable results from any initialization of active contour models.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.6
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• pp.152-157
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• 2000

In the control of CNC machine tools, it is significant for precise machining to reduce the contour error. The object of servo-control is reduction of contour error and tracking error. In past studies, there were two approaches to control a servo-system. One was to eliminate axial tracking errors, and the other was to control contour errors. The Cross-coupled controller(CCC) was introduced fro ma veiwpoint of contour error model. Recently, for machining part with free form surfaces, we propose a new contour error model based on curve interpolator. It is presented here that performance of CCC using proposed model is enhanced. Therefore, we can make more precise parts with the curve interpolator and the new contour error model.

• Journal of the Korea Society of Computer and Information
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• v.3 no.1
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• pp.81-94
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• 1998

In order to extract the contour of interesting object in the image, Kass suggested the Active Contour Model called "Snakes". Snakes is a model which defines the contour of image energy. It also can find the contour of object by minimizing these energy functions. The speed of this model is slow and this model is sensitive of initialization. In order to improve these problems, Gunn extracted the accurate contour by using two initialization. and operated to less sensitive of initialization. This method could extract more accurate contour than the existing method, but it had no effect in the speed and it was sensitive of noise. This paper applied to the Energy Minimization Algorithm about only the pixel within the window applying the window of 8$\times$8 size at each contour point consisting Snakes in order to solve these problems. The method offered in this paper is applied to extract the contour of original image and cup image added to gaussian noise. By tracking the face using this offered method, it is applied to virtual reality and motion tracking. tracking.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.1
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• pp.40-47
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• 2004

This paper proposes a three-axis coupling controller designed to improve the contouring accuracy in machining of 3D nonlinear contours. The proposed coupling controller is based on an innovative 3D contour error model and a PID control law. The novel contour error model provides almost exact calculation of contour errors in real-time for arbitrary contours and can be integrated with any type of existing interpolator. In the proposed method, three axes of motion are coordinated by the proposed coupling controller along with a proportional controller for each axis. The proposed contour error model and coupling controller are evaluated through computer simulations. The simulation results show that the proposed 3-axis coupling controller with the new contour error model substantially can improve the contouring accuracy by order of magnitude compared with the existing uncoupled controllers in high-speed machining of nonlinear contours.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.123-130
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• 2000

In this paper, we propose an extracting method of moving object contour using active contour model from image sequences acquired by fixed camera. We use an adaptive background model for robust processing in surrounding conditions. Object segmentation model detects pixels thresholded from local difference image between background and current image and extracts connected regions. Noises in boundary area of moving object we eliminated by morphological filter. The contour of segmented object is corrected by using active contour model for extracting accurate boundary of moving object. We apply the proposed method to highway image sequences and show the results of simulation.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.116-118
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• 1997

In hippocampal morphology Abnormalities, including unilateral or bilateral volume loss, are known to occur in epilepsy, Alzheimer's disease, and in certain amnestic syndromes. To detect such abnormalities in hippocampal morphology, we present a method that combines region growing and dynamic contour model to detect hippocampus from MRI brain data. The segmentation process is performed two steps. First region growing with a seed point is performed in the region of hippocampus and the initial contour of dynamic contour model is obtained. Second, the initial contour is modified on the basis of criteria that integrate energy with contour smoothness and the image gradient along the contour. As a result, this method improves fairly sensitivity to the choice of the initial seed point, which is often seen by conventional contour model. The power and practicality of this method have been tested on two brain datasets. Thus, we have developed an effective algorithm to extract hippocampus from MRI brain data.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.47-50
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• 2006

An active contour model has been used to detect the edges in a still image. In order to apply the active contour model to edge detection, the energy function which consists of internal, external and image energies should be defined. After defining the energy function, the edge of an object is detected through minimization of the value of the energy function. In this paper, the modified internal energy function is proposed to improve the convergence of the energy function when the active contour model is applied to the tracking of deformable objects using the greedy algorithm. In order to show the performance of the proposed energy function, experiments were carried out for the still and animated images.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.666-670
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• 2011

In this paper, we propose a stable active contour based tracking method which utilizes the bimodal segmentation technique to obtain a background color diminished image frame. The proposed method overcomes the drawback of the Mansouri model which is liable to fall into a local minimum state when colors appear in the background that are similar to the target colors. The Mansouri model has been a foundation for active contour based tracking methods, since it is derived from a probability based interpretation. By stabilizing the model with the proposed speed function, the proposed model opens the way to extend probability based active contour tracking for practical applications.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.490-492
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• 2002

본 논문은 물 속 유충인 깔따구의 움직임을 관찰한 데이터에 Active Contour Model을 적용하여 깔따구 상태의 특징을 추출하는 방법을 제안한다. 1987년 소개된 Active Contour Model은 주어진 영상에 놓인 커브를 그 커브에 의해 분할된 영상의 에너지 값을 최소화하는 방향으로 진화하게 함으로써 영상 내 객체의 경계를 찾게 하는 영상분할 방법이다. Chan과 Vese에 의해 개선된 Model을 이용하여 다이아지논이 처리되기 전과 후의 깔따구 행동 패턴의 특징을 찾아낸다. 우선 깔따구의 움직임 궤적을 0.25초를 간격으로 관찰하여 구해진 속도벡터의 위상영상을 만든다.그리고 위상영상에 Active Contour를 두어 진화시키면서 시간에 따라 감소하는 에너지 값의 그래프에서 구해진 기울기로 깔따구 행동 패턴의 특징을 추출한다.