• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.271-276
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• 1993

Here in the present paper. A methodology for understanding scenes which includes moving objects in it, in the framework of notion of concepts. First by conceptualizing, understanding an object which is an element of a scene will be described. Then how to know the direction to which that object is heading will be discussed. Further, the methodology proposed, for understanding conceptually the motion of an object will be described utilizing the above knowledge of direction.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.141-144
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• 2002

This paper describes a new feature based tracking system that can track moving objects with a pan-tilt camera. We extract corner features of the scene and tracks the features using filtering, The global motion energy caused by camera movement is eliminated by finding the maximal matching position between consecutive frames using Pyramidal template matching. The region of moving object is segmented by clustering the motion trajectories and command the pan-tilt controller to follow the object such that the object will always lie at the center of the camera. The proposed system has demonstrated good performance for several video sequences.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.905-910
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• 2008

In this paper, a circular motion table which is able to simulate movement of object is designed and the experiment of control system using circular motion table is presented. Circular motion table is consisted of three axes changed on length and of ball splines which keep vertical centre axis of circular plate. Variable length of three axes make circular plate incline as vertical centre axis is kept on vertical center axis of circular motion table. It is designed that control system drives three servo motor, that is, make change length of axis simultaneously or independently. And this paper presents example of flight simulation using circular motion table. it will contribute toward nurture expert manpower of aerospace/robotics to popularize circular motion table and make an experiment using it.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.129-132
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• 2002

In this paper, we propose a 3-D animation system to track and analyze motion of the human object. The proposed system consists of two separate layers: motion analysis layer and 3-D model registration layer. Following the MPEG-4 SNHC standard, we generate object motion using body definition and animation parameters. In the implemented system, we acquire human motion data from a single camera and extract body definition parameters from arbitrary VRML human models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.221-230
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• 2002

This paper presents a new moving object segmentation algorithm using markov random field. The algorithm is based on signal detection theory. That is to say, motion of moving object is decided by binary decision rule, and false decision is corrected by markov random field model. The procedure toward complete segmentation consists of two steps: motion detection and object segmentation. First, motion detection decides the presence of motion on velocity vector by binary decision rule. And velocity vector is generated by optical flow. Second, object segmentation cancels noise by Bayes rule. Experimental results demonstrate the efficiency of the presented method.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.217-226
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• 2006

Grasping and manipulation by hands can be considered as one of inevitable functions to achieve the performances desired in humanoid operations. When a humanoid robot manipulates an object by his hands, each finger should be well-controlled to accomplish a precise manipulation of the object grasped. So, the trajectory of each joint required for a precise finger motion is fundamentally necessary to be planned stably. In this sense, this paper proposes an effective joint motion planning method for humanoid fingers. The proposed method newly employs a bio-mimetic concept for joint motion planning. A suitable model that describes an interphalangeal coordination in a human finger is suggested and incorporated into the proposed joint motion planning method. The feature of the proposed method is illustrated by simulation results. As a result, the proposed method is useful for a facilitative finger motion. It can be applied to improve the control performance of humanoid fingers or prosthetic fingers.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.277-285
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• 2016

Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.173-181
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• 2004

In this paper, we propose a system for automatic moving object detection and tracking in sequence images acquired from a moving camera. The proposed algorithm consists of moving object detection and its tracking. Moving object can be detected by integration of BBME and DD method We segment the detected object using histogram back projection, match it using histogram intersection, extract and track it using XY-projection. Computer simulation results have shown that the proposed algorithm is reliable and can successfully detect and track a moving object on image sequences obtained by a moving camera.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.427-432
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• 2018

This paper uses a recursive least squares method to estimate the projectile motion trajectory of an object in real time. The equations of motion of the object are obtained considering the air resistance which occurs in the actual experiment environment. Because these equations consider air resistance, parameter estimation of nonlinear terms is required. However, nonlinear recursive least squares estimation is not suitable for estimating trajectory of projectile in that it requires a lot of computation time. Therefore, parameter estimation for real-time trajectory prediction is performed by recursive least square estimation after using Taylor series expansion to approximate nonlinear terms to polynomials. The proposed method is verified through experiments by using VICON Bonita motion capture system which can get three dimensional coordinates of projectile. The results indicate that proposed method is more accurate than linear Kalman filter method based on the equations of motion of projectile that does not consider air resistance.