• 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.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.337-343
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• 2014

This paper proposes a minimal contour tracking algorithm that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. This algorithm perform detection for object tracking and when it transmit image data to server from camera, it minimized communication load by reducing quantity of transmission data. This algorithm use minimal tracking area based on the kinematics of the object. The modeling of object's kinematics allows for pruning out part of the tracking area that cannot be mechanically visited by the mobile object within scheduled time. In applications to detect an object in real time,when transmitting a large amount of image data it is possible to reduce the transmission load.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1972-1978
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• 2010

AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.30-34
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• 1998

Path Planning is one of the important fields in robot technologies. Local path planning may be done in on-line modes while recognizing an environment of robot by itself. In dynamic environments to obtain fluent information for environments vision system as a sensing equipment is a one of the most necessary devices for safe and effective guidance of robots. If there is a predictor that tells what future sensing outputs will be, robot can respond to anticipated environmental changes in advance. The tracking of obstacles has a deep relationship to the prediction for safe navigation. We tried to deal with active contours, that is snakes, to find out the possibilities of stable tracking of objects in image plane. Snakes are defined based on energy functions, and can be deformed to a certain contour form which would converge to the minimum energy states by the forces produced from energy differences. By using point algorithm we could have more speedy convergence time because the Brent's method gives the solution to find the local minima fast. The snake algorithm may be applied to sequential image frames to track objects in the images by these characteristics of speedy convergence and robust edge detection ability.

• Journal of the Korean Geotechnical Society
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• v.15 no.3
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• pp.27-40
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• 1999

In limit equilibrium methods(LEM), all methods employ the same definition of the safety factor as a ratio of the shear strength of the soil to the shear stress required for equilibrium, employing certain assumptions with regard to equilibrium. In addition, in the conventional finite element method of analysis, the minimum safety factor is obtained assuming certain slip surfaces after the state of stress are found. Although the stress states are obtained from the finite element method(FEM), the slope stability analysis follows the conventional method that assumes a potential slip surface. In this study, a slope stability analysis based on FEM is developed to locate the slip surface by tracking the weakest points in the slope based on the local safety factor considering the magnitude and direction of the shear stresses. It has also been applied to be compared with the slip surfaces predicted by LEM. A computer program has been developed to draw contour lines of the local safety factors automatically. This method is illustrated through a simple hypothetical slope, a natural soil slope, and a dam slope. The developed method matches very well with the conventional LEM methods, with slightly lower global safety factors.