• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.23-34
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• 2004

In this paper we propose a feature point tracking algorithm using optical flow under non-prior taming active feature model (NPT-AFM). The proposed algorithm mainly focuses on analysis non-rigid objects[1], and provides real-time, robust tracking by NPT-AFM. NPT-AFM algorithm can be divided into two steps: (i) localization of an object-of-interest and (ii) prediction and correction of the object position by utilizing the inter-frame information. The localization step was realized by using a modified Shi-Tomasi's feature tracking algoriam[2] after motion-based segmentation. In the prediction-correction step, given feature points are continuously tracked by using optical flow method[3] and if a feature point cannot be properly tracked, temporal and spatial prediction schemes can be employed for that point until it becomes uncovered again. Feature points inside an object are estimated instead of its shape boundary, and are updated an element of the training set for AFH Experimental results, show that the proposed NPT-AFM-based algerian can robustly track non-rigid objects in real-time.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.281-288
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• 2016

The natural moving objects are the most non-rigid shapes with randomly time-varying deformation, and its types also very diverse. Methods of non-rigid shape reconstruction have widely applied in field of movie or game industry in recent years. However, a realistic approach requires moving object to stick many beacon sets. To resolve this drawback, non-rigid shape reconstruction researches from input video without beacon sets are investigated in multimedia application fields. In this regard, our paper propose novel CPSRF(Chained Partial Stereo Rigid Factorization) algorithm that can reconstruct a non-rigid 3D shape. Our method is focused on the real-time reconstruction of non-rigid 3D shape and motion from stereo 2D video sequences per frame. And we do not constrain that the deformation of the time-varying non-rigid shape is limited by a Gaussian distribution. The experimental results show that the 3D reconstruction performance of the proposed CPSRF method is superior to that of the previous method which does not consider the random deformation of shape.

• 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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• 2001.06d
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• pp.85-88
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• 2001

Unlike rigid objects or This paper developes the algorithm for segmenting gaseous objects on an image plane. Unlike rigid objects or solid non-rigid objects, gaseous objects vary in density even within single-object regions and the edge intensity differs at different locations. So, an edge detector may detect only strong edges and detected edges may be an incomplete parts of an whole object's boundary. Due to this property of gaseous objects, it is not easy to distinguish the real edges of gaseous objects from the noisy-like edges such as leaves. Our algorithm uses two criteria of edge intensity and edge's line connectivity, then applies fuzzy set so as to obtain the proper threshold of the edge detector

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.236-246
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• 2006

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks such as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. In this paper, the development of new snake model called 'adaptive color snake model (ACSM)' for segmentation and tracking is introduced. The simple operation makes the algorithm runs in real-time. For robust tracking, the condensation algorithm was adopted to control the parameters of ACSM. The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.127-136
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• 2003

In this paper, we propose a robust object tracking algorithm based on model and edge, using deformed template and Level-Set theory. The proposed algorithm can track objects in case of background variation, object flexibility and occlusions. First we design a new potential difference energy function(PDEF) composed of two terms including inter-region distance and edge values. This function is utilized to estimate and refine the object shape. The first step is to approximately estimate the shape and location of template object based on the assumption that the object changes its shape according to the affine transform. The second step is a refinement of the object shape to fit into the real object accurately, by using the potential energy map and the modified Level-Set speed function. The experimental results show that the proposed algorithm can track non-rigid objects under various environments, such as largely flexible objects, objects with large variation in the backgrounds, and occluded objects.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.445-448
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• 2003

In this paper, we present a hierarchical approach of an enhanced active shape model for video tracking. Kalman filter is used. To estimate a dynamic shape in video object tracking. The experimental results show that the proposed hierarchical active shape model using Kalman filter is efficient.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.102-110
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• 2015

In this paper, adaptive matching method using the same features for rigid and deformable object images is proposed. Firstly, we determine whether the two images are matched or not using the geometric verification and generate the matching information. Decision boundary which separates deformable matching-pair from non-matching pair is obtained through statistical analysis of matching information. The experimental result shows that the proposed method lowers the computational complexity and increases the matching accuracy compared to the existing method.