• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.268-268
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• 2012

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1990-1993
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• 2005

Stereovision is known to be one of the most important tools for robot vision systems. Previously, 2D active contour model has been applied to stereovision by defining the contour on the 3D space instead of image plane. However, the proposed model is still that of curve so that some complex shapes such as surfaces with high curvature can not be properly estimated because of occlusion phenomena. In this paper, the authors extend the curve model to the surface model. The surface is approximated by polygons and new energy function and its optimization method for surface estimation is proposed. Its effectiveness is examined by experiments with real stereo images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1840-1855
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• 2015

Troubles are often met when traditional active contours extract boundaries of medical images with inhomogeneous bias and various noises. Focusing on such a circumstance, a localized mutual information active contour model is discussed in the paper. By defining neighborhood of each point on the level set, mutual information is introduced to describe the relationship between the zero level set and image field. A driving energy term is then generated by integrating all the information. In addition, an expanding energy and internal energy are designed to regularize the driving energy. Contrary to piecewise constant model, new model has a better command of driving the contours without initialization.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.105-114
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• 2011

The snake algorithm is widely adopted to track objects by extracting the active contour of the object from background. However, it fails to track the target converging to the background if there exists background whose gradient is greater than that of the pixels on the contour. Also, the contour may shrink when the target moves fast and the snake algorithm misses the boundary of the object in its searching window. To alleviate these problems, we propose an improved algorithm that can track object contour more robustly. Firstly, we propose two external energy functions, the edge energy and the contrast energy. One is designed to give more weight to the gradient on the boundary and the other to reflect the contrast difference between the object and background. Secondly, by computing the motion vector of the contour from the difference of the two consecutive frames, we can move the snake pointers of the previous frame near the region where the object boundary is probable at the current frame. Computer experiments show that the proposed method is more robust to the complicated background than the previously known methods and can track the object with fast movement.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.697-704
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• 2003

In this paper, the visual tracking system for arbitrary shaped moving object is proposed. The established tracking system can be divided into model based method that needs previous model for target object and image based method that uses image feature. In the model based method, the reliable tracking is possible, but simplification of the shape is necessary and the application is restricted to definite target mod el. On the other hand, in the image based method, the process speed can be increased, but the shape information is lost and the tracking system is sensitive to image noise. The proposed tracking system is composed of the extraction process that recognizes the existence of moving object and tracking process that extracts dynamic characteristics and shape information of the target objects. Specially, active contour model is used to effectively track the object that is undergoing shape change. In initializatio n process of the contour model, the semi-automatic operation can be avoided and the convergence speed of the contour can be increased by the proposed effective initialization method. Also, for the efficient solution of the correspondence problem in multiple objects tracking, the variation function that uses the variation of position structure in image frame and snake energy level is proposed. In order to verify the validity and effectiveness of the proposed tracking system, real time tracking experiment for multiple moving objects is implemented.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.521-526
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• 2003

In this paper, we propose a geometric active contour model based on intensity information and curve evolution for detecting region boundaries. We put boundary extraction problem as the minimization of the difference between the average intensity of the region and the intensity of the expanding closed curves. We used level set theory to implement the curve evolution for optimal solution. It offered much more freedom in the initial curve position than a general active contour model. Our methods could detect regions whose boundaries are not necessarily defiened by gradient compared to general edge based methods and detect multiple boundaries at the same time. We could improve the result by using anisotropic diffusion filter in image preprocessing. The performance of our model was demonstrated on several data sets like CT and MRI medical images.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.17-22
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• 2002

This paper used an active contour model which was based on level set algorithms and bidirectional curve evolution theory in order to track the shape of the heart acting continuously. Most active contour models would be failed in boundary extraction because of their unstable movement in the edge gap locations. In this paper, we suggest a new active contour model using only image intensity value and additional constraint needed for stable extraction. Our model was successfully run on either shape extraction or object tracking without any position constraints of initial curve. Also demonstrated stable movements and showed good results at weak or missing boundary locations.

• Journal of Biomedical Engineering Research
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• v.24 no.2
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• pp.55-60
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• 2003

For quantitative analysis of the cardiac diseases. it is necessary to segment the left-ventricle (LY) in MR (Magnetic Resonance) cardiac images. Snake or active contour model has been used to segment LV boundary. However, the contour of the LV front these models may not converge to the desirable one because the contour may fall into local minimum value due to image artifact inside of the LY Therefore, in this paper, we Propose the Preprocessing method using k-means clustering and merging algorithms that can improve the performance of the active contour model. We verified that our proposed algorithm overcomes local minimum convergence problem by experiment results.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.901-911
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• 2009

In this paper, we propose a construction technique of two-level composite gradient map to automatically track a face with large movement in successive frames. Our method is composed of three main steps. First, the gradient maps with two-level resolution are generated for fast convergence of active contour. Second, to recognize the variations of face between successive frames and remove the neighbor background, weighted composite gradient map is generated by combining the composite gradient map and difference mask of previous and current frames. Third, to prevent active contour from converging local minima, the energy slope is generated by using closing operation. In addition, the fast closing operation is proposed to accelerate the processing time of closing operation. For performance evaluation, we compare our method with previous active contour model-based face tracking methods using a visual inspection, robustness test and processing time. Experimental results show that our method can effectively track the face with large movement and robustly converge to the optimal position even in frames with complicated background.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.195-199
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• 2002

본 논문에서는 주어진 입력 이미지에서 얼굴 영역을 검출하기 위한 액티브 컨투어 모델(active contour models)을 제안한다. 제안한 모델은 스킨 칼라 모델(skin color model)에 의해 표현되는 사람 얼굴의 칼라 정보를 이용한다. 본 논문에서는 첨점(cusps), 모서리 (corners), 그리고 자동 위상 변화(automatic topological changes)를 고려한 레벨 셋 메소드(level set method)를 사용하여 액티브 컨투어를 진화시킨다. 실험 결과는 제안한 방법이 얼굴 영역 검출에 효과가 있음을 보여준다.