• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.437-441
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• 1997

In the conventional remote monitoring system, a user in front of a computer monitor can acquire only 2 dimensional visual information in a passive way. Thus, even thoght the user finds an interesting object from the video image, helshe can hardly acquire additional information on the object such as name. 311 shape, etc. In this paper, an active monitoring system that shows additional information on the selected object is proposed. The active remote monitoring system can calculate the 3D position of the object that is selected in the video images. Then, using the 3D position of the object, other information on the object can be retrieved from the database and shown on the screen. To calculate the 3D position of the object, 2 CCD cameras that can be tilted and panned using 3 stepping motors are used. The algorithm of 3D position calculation and the result of experiments are explained.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.60-70
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• 2000

In this paper a 3D position measuring device that finds the 3D position of an arbitarily placed object using a camersa system is introduced. The camera system consists of three stepping motors and a CCD camera and a laser. The viewing direction of the camera is controlled by two stepping motors (pan and tilt motors) and the direction of a laser is also controlled by a stepping motors(laser motor). If an object in a remote place is selected from a live video image the x,y,z coordinates of the object with respect to the reference coordinate system can be obtained by calculating the distance from the camera to the object using a structured light scheme and by obtaining the orientation of the camera that is controlled by two stepping motors. The angles o f stepping motors are controlled by a SGI O2 workstation through a parallel port. The mathematical model of the camera and the distance measuring system are calibrated to calculate an accurate position of the object. This 3D position measuring device can be used to acquire information that is necessary to monitor a remote place.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.26-34
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• 1999

This study shows an alternative method for the determination of object's position, based on a computer vision method. This approach develops the vision system model to define the reciprocal relationship between the 3-D real space and 2-D image plane. The developed model involves the bilinear six-view parameters, which is estimated using the relationship between the camera space location and real coordinates of known position. Based on estimated parameters in independent cameras, the position of unknown object is accomplished using a sequential estimation scheme that permits data of unknown points in each of the 2-D image plane of cameras. This vision control methods the robust and reliable, which overcomes the difficulties of the conventional research such as precise calibration of the vision sensor, exact kinematic modeling of the robot, and correct knowledge of the relative positions and orientation of the robot and CCD camera. Finally, the developed vision control method is tested experimentally by performing determination of object position in the space using computer vision system. These results show the presented method is precise and compatible.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.5
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• pp.9-11
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• 2001

• The Journal of Korea Robotics Society
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• v.2 no.2
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• pp.129-136
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• 2007

This paper introduces a position-based robust visual servoing method which is developed for operation of a human-like robot with two arms. The proposed visual servoing method utilizes SIFT algorithm for object detection and CAMSHIFT algorithm for object tracking. While the conventional CAMSHIFT has been used mainly for object tracking in a 2D image plane, we extend its usage for object tracking in 3D space, by combining the results of CAMSHIFT for two image plane of a stereo camera. This approach shows a robust and dependable result. Once the robot's task is defined based on the extracted 3D information, the robot is commanded to carry out the task. We conduct several position-based visual servoing tasks and compare performances under different conditions. The results show that the proposed visual tracking algorithm is simple but very effective for position-based visual servoing.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1027-1038
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• 2005

Object recognition in the field of robotics generally has depended on a computer vision system. Recently, RFID(Radio Frequency IDentification) has been suggested as technology that supports object recognition. This paper, introduces the advanced RFID-based recognition using a novel tag which is named a 3D tag. The 3D tag was designed to facilitate object recognition. The proposed RFID system not only detects the existence of an object, but also estimates the orientation and position of the object. These characteristics allow the robot to reduce considerably its dependence on other sensors for object recognition. In this paper, we analyze the characteristics of the 3D tag-based RFID system. In addition, the estimation methods of position and orientation using the system are discussed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.12
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• pp.1002-1016
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• 1990

This paper described design and implementation of stereo vision system, and also, proposed method for displacement estimation of 3-D moving object using this system. The extraction of moving object is obtained by difference image algorithm. Geometrical position of 3-D moving object is calculated form the mapping of center area of two's 2-D object. 3-D coordinate position produced space depth, moving velociity, distance, moving track and proved displacement estimation of 3-D moving object.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.62-67
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• 2005

Object recognition in the field of robotics generally has depended on a computer vision system. Recently, RFID(Radio Frequency IDentification) technology has been suggested to support recognition and has been rapidly and widely applied. This paper introduces the more advanced RFID-based recognition. A novel tag named 3D tag, which facilitates the understanding of the object, was designed. The previous RFID-based system only detects the existence of the object, and therefore, the system should find the object and had to carry out a complex process such as pattern match to identify the object. 3D tag, however, not only detects the existence of the object as well as other tags, but also estimates the orientation and position of the object. These characteristics of 3D tag allows the robot to considerably reduce its dependence on other sensors required for object recognition the object. In this paper, we analyze the 3D tag's detection characteristic and the position and orientation estimation algorithm of the 3D tag-based RFID system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1920-1925
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• 2004

This paper describes a new method for 3D object recognition which used surface segment-based stereo vision. The position and orientation of an objects is identified accurately enabling a robot to pick up, even though the objects are multiple and partially occluded. The stereo vision is used to get the 3D information as 3D sensing, and CAD model with its post processing is used for building models. Matching is initially performed using the model and object features, and calculate roughly the object's position and orientation. Though the fine adjustment step, the accuracy of the position and orientation are improved.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.535-540
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• 2009

Recently, autonomous robots which can achieve the complex tasks have been required with the advance of robotics. Advanced robot vision for recognition is necessary for the realization of such robots. In this paper, we propose a method to recognize an object in the actual environment. We assume that a 3D-object model used in our proposal method is the voxel data. Its inside is full up and its surface has color information. We also define the word "recognition" as the estimation of a target object's condition. This condition means the posture and the position of a target object in the actual environment. The proposal method consists of three steps. In Step 1, we extract features from the 3D-object model. In Step 2, we estimate the position of the target object. At last, we estimate the posture of the target object in Step 3. And we experiment in the actual environment. We also confirm the performance of our proposal method from results.