• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.10B
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• pp.1419-1427
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• 2001

본 논문은 연속영상에서 non-rigid object를 자동으로 분할하고 알고리즘을 제안하였다. Non-rigid object는 형태의 변화가 일정하기 않은 object로서 기존의 분할 알고리즘과는 다른 새로운 분할 알고리즘을 필요로 한다. 본 논문에서는 특히 구름이나 연기와 같이 형태의 변화가 큰 non-rigid object를 자동으로 분할하는 알고리즘을 제안하였다. 제안된 알고리즘은 공간분할, 시간분할, 그리고 공간분할과 시간분할의 결합의 세 가지 단계로 구성되어 있다. 공간분할은 영상에서 픽셀의 intensity를 마코프 랜덤 필드로 가정하고 에너지 최소화를 통해 영상을 분할한다. 시간분할은 속도벡터를 기반으로 하여 움직임이 있는 영역만을 분할한다. 마지막으로 공간분할과 시간분할을 결합하여 non-rigid object의 최종적인 분할을 수행한다. 실험결과, 제안된 알고리즘은 연속영상에서 non-rigid object를 자동으로 분할함을 확인할 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.17-31
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• 2002

The new MPEG-4 video coding standard enables content-based functionalities. Image segmentation is an indispensable process for it. This paper addresses an effective segmentation of non-rigid objects. Non-rigid objects are deformable objects with fuzzy, blurred and indefinite boundaries. So it is difficult to segment deformable objects precisely. In order to solve this problem, we propose an effective segmentation of non-rigid objects using watershed algorithms in still pictures. And we propose an automatic segmentation through intra-frame and inter-frame segmentation process in video sequences. Automatic segmentation preforms boundary-based and region-based segmentation to extract precise object boundaries.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.109-116
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• 2009

In this thesis, an object tracking method based on the active feature model and the optical flow in stereo images is proposed. We acquired the translation information of object of interest and the features of object by utilizing the geometric information and depth of stereo images. Tracking performance is improved for the occlude object with this information by predicting the movement information of features of the occlude object. Rigid and non-rigid objects are experimented. From the result of experiment, the OOI can be real-time tracked from complicate back ground. Besides, we got the improved result of object tracking in any occlusion state, no matter what it is rigid or non-rigid object.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.169-171
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• 2001

Unlike rigid objects, the edge intensity of a gaseous object is various along the object boundary (edge intensities of some pixels on a gaseous object boundary are weaker than those of small rigid objects or noise itself). Therefore, the conventional edge detectors may not adequately detect boundary-like edge pixels for gaseous objects. In this paper A new methodology for segmenting gaseous object images is introduced. Proposed method consists of fuzzy-based boundary detector applicable to gaseous as well as rigid objects and concave region filling to recover object regions.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.639-642
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• 2000

본 논문에서는 구름이나 연기와 같은 non-rigid object에 대한 영역 분할 방식에 대해 연구하였다. Non-rigid object의 효과적인 영역 분할을 위해서 object의 윤곽선을 정확히 파악해 낼 수 있는 장점을 가진 watershed 알고리즘을 사용하였다. 하지만 이 알고리즘은 object가 많은 영역으로 분할되는 oversegmentation 현상이 발생하여 본 논문에서는 pre, post-processing을 통해 이 oversegmentation 현상을 극복하고자 하였다. Pre-processing에서는 noise를 제거하고 영상을 단순화하면서 정확한 gradient magnitude를 구할 수 있는 방법에 대해서, post-processing에서는 통계적인 분석을 통한 region merging을 이용하여 object를 최적화 상태로 찾아줄 수 있는 방법에 대하여 연구하였다.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.171-178
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• 2014

This paper presents dynamic modelling of a virtual object in augmented reality environments when external forces are applied to the object in real-time fashion. In order to simulate a natural behavior of the object we employ the theory of Newtonian physics to construct motion equation of the object according to the varying external forces applied to the AR object. In dynamic modelling process, the physical interaction is taken placed between the augmented object and the physical object such as a haptic input device and the external forces are transferred to the object. The intrinsic properties of the augmented object are either rigid or elastically deformable (non-rigid) model. In case of the rigid object, the dynamic motion of the object is simulated when the augmented object is collided with by the haptic stick by considering linear momentum or angular momentum. In the case of the non-rigid object, the physics-based simulation approach is adopted since the elastically deformable models respond in a natural way to the external or internal forces and constraints. Depending on the characteristics of force caused by a user through a haptic interface and model's intrinsic properties, the virtual elastic object in AR is deformed naturally. In the simulation, we exploit standard mass-spring damper differential equation so called Newton's second law of motion to model deformable objects. From the experiments, we can successfully visualize the behavior of a virtual objects in AR based on the theorem of physics when the haptic device interact with the rigid or non-rigid virtual object.

• Journal of the Korea Computer Graphics Society
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• v.6 no.1
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• pp.11-17
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• 2000

We present collision-handling method that includes self-penetration in the case of the colliding between rigid and deformable objects. The collision between objects is detected through the overlap test to the hierarchical structures of the objects. For detecting the collision between the objects at in-between frame, we try overlap test using the structures of a dummy and the rigid objects in addition to the test between the rigid and deformable objects. The dummy object is made from the rigid objects moving direction. When collision occurs, a deformable object must be deformed, as the object doesn't permit penetration. Self-penetration may occur during the object is deformed. It is rapidly detected between the object and a dummy object of another type. The dummy object is made from the object's deformation area between two continuous frames. We constrain the object is deformed until it is self-contacted. Our method can be applied without concerning of the shape of a object.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2104-2107
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• 2003

In this paper, we propose the method Model based Non-Rigid Moving Object Tracking. Motion based method becomes difficult to predict precisely when motion gets larger, so that we can solve such difficultly with regarding the moving object as a model. In the model based method, it should be concerned about setting initial model and updating its model in each frame. We used SNAKE in a way to set the initial model, and also proposed a modified SNAKE to handle the previous SNAKE problems. Moreover, with the elliptical setting, we made the initializing process automatically which is highly subject to change in measuring the performance of SNAKE. We used the Hausdorff distance to identify models in each frame. Through our experiments, our Proposed algorithm does effective work in Non-Rigid Moving Object Tracking.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.183-186
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• 2001

This paper for the method that automatically extracts moving object of the video image is presented. In order to extract moving object, it is that velocity vectors correspond to each frame of the video image. Using the estimated velocity vector, the position of the object are determined. the value of the coordination of the object is initialized to the seed, and in the image plane, the moving object is automatically segmented by the region growing method and tracked by the range of intensity and information about Position. As the result of an application in sequential images, it is available to extract a moving object.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.1998-2009
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• 1998

An effective algorithm for tracking rigid or non-rigid moving object(s) which segments local moving parts from image sequence in the presence of backgraound motion by camera movenment, predicts the direction of it, and tracks the object is proposed. It requires no camera calibration and no knowledge of the installed position of camera. In order to segment the moving object, feature points configuring the shape of moving object are firstly selected, feature flow field composed of motion vectors of the feature points is computed, and moving object(s) is (are) segmented by clustering the feature flow field in the multi-dimensional feature space. Also, we propose IRMAS, an efficient algorithm that finds the convex hull in order to cinstruct the shape of moving object(s) from clustered feature points. And, for the purpose of robjst tracking the objects whose movement characteristics bring about the abrupt change of moving trajectory, an improved order adaptive lattice structured linear predictor is used.