• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.669-672
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• 2003

3D object recognition is difficult but important in computer vision. The important thing is to understand about the relationship between a geometric structure in three dimensions and its image projection. Most 3D recognition systems construct models either manually or by training the pose and orientation of the objects. But both approaches are not satisfactory. In this paper, we focus on a commercial CAD model as a third type of model building for vision. The models are expressed in Initial Graphics Exchanges Specification(IGES) output and reconstructed in a pinhole camera coordinate.

• Journal of Korea Multimedia Society
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• v.5 no.3
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• pp.272-280
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• 2002

In this paper, we present a method to extract a moving object and to measure the distance to it by using the stereo vision system. The moving factor is to be extracted through a match of a pixel unit for the moving object where the adaptive threshold is effectively dealt with to remove changes in the brightness of the image. The distance to moving object is measured by using a stereo vision system which employs a parallel camera. The experimental results show that the proposed algorithm could be effectively applied to distance measurement to moving object because it has an average error of one percent.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1124-1128
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• 1995

This paper presents the appplication of computer vision for the purpose of determing the position of the unknown point in the plane. The presented contrik method is estimate the six view parameters reqresenting the relationship between the image plane coordinates and the real physical coordinates. The estimation of six parameters is indispensable for transforming the 2-dimensional camera coordinates to the 3-dimensional spatial coordinates. Then, the position of unknown point is estimated based on the estimated parameters depending on the cameras. The suitability of this control scheme is demonstrated experimentally by determining of position the unknown point in the plane.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.68-73
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• 1999

Generally, most of machining processes are done according to the dimention of the draft made by CAD. However, there are many cases that a sample is given without the draft because of the simplicity of the shape in the machining of 2D objects. To cut the same shape as the given sample, this paper proposes the method to extract the geometric information about a large sample using the robot vision and to draw the demensional draft for the machining. Because the resolution of one frame in the vision system is too low, it is necessary to set up a camera according to the desired resolution and to capture the image moving along the contour. And the overall outline can be compounded of the sequentially captured images. In the experiment, we compared the product after the cutting with the original sample and found that the size of two objects was coincided within the allowed error bound.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.184-191
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• 2004

In this study, an inspection system for automotive parts using machine vision has been developed and presented. The system is comprised of six analog CCD cameras, frame grabber, and mechanism that loads the automotive parts to the system for the inspection. An Image processing algorithm for detecting eight different types of defects of oil-seals are developed, and the effectiveness of the algorithm is experimentally verified. Inspection process is completed in 1 second with acceptable accuracy. It is envisaged that this inspection system will have a wide application in the automotive part manufacturing industry in the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.4
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• pp.817-819
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• 2007

The purpose of this work is to develop a measurement system of the pendulum test with minimal restriction of experimental environment and little influence of noise. In this work, we developed a vision system without any line between markers and a camera. The system performance is little influenced by the experimental environment, if light are sufficient to recognize markers. For the validation of the system, we compared knee joint angle trajectories measured by the developed system and by the magnetic sensor system during the nominal pendulum test and the maximum speed voluntary knee joint rotation. The joint angle trajectories of the developed system during both tests matched well with those of the magnetic system. Therefore, we suggest the vision system as an alternative to the previous systems with limited practicality for the pendulum test.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.39-39
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• 2011

This study presents an effective vision-based system for cable bridge damage detection. In theory, cable bridges need to be inspected the outer as well as the inner part. Starting from August 2010, a new research project supported by Korea Ministry of Land, Transportation Maritime Affairs(MLTM) was initiated focusing on the damage detection of cable system. In this study, only the surface damage detection algorithm based on a vision-based system will be focused on, an overview of the vision-based cable damage detection is given in Fig. 1. Basically, the algorithm combines the image enhancement technique with principal component analysis(PCA) to detect damage on cable surfaces. In more detail, the input image from a camera is processed with image enhancement technique to improve image quality, and then it is projected into PCA sub-space. Finally, the Mahalanobis square distance is used for pattern recognition. The algorithm was verified through laboratory tests on three types of cable surface. The algorithm gave very good results, and the next step of this study is to implement the algorithm for real cable bridges.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.163-165
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• 2009

Automatic Vision Inspection(AVI) systems automatically detect defect features and measure their sizes via camera vision. It is important to predict the performance of an AVI to meet customer's specification in advance. In this paper, we propose a statistical method for prediction of false alarm rate regarding inconsistency of defect size measuremet process. We only need are a simple experimental trial for repeated defect size measurement test. The statistical features from the experiement are utilized in the prediction process. Therefore, the proposed method is swift and easy to implement and use. The experiment shows a close prediction compared to manual inspection results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.420-425
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• 2003

Generally, reverse engineering means getting CAD data from unidentified shape using vision or 3D laser scanner system. In this paper, we studied unidentified model by machine vision based reverse engineering system to get information about in-processing model. Recently, vision technology is widely used in current factories, because it could inspect the in-process object easily, quickly, accurately. The following tasks were mainly investigated and implemented. We obtained more precise data by corning camera's distortion, compensating slit-beam error and revising acquired image. Much more, we made similar curves or surface with B-spline approximation for precision. Until now, there have been many case study of shape recognition. But it was uncompatible to apply to the field, because it had taken too many processing time and has frequent recognition failure. This paper propose recognition algorithm that prevent such errors and give applications to the field.