• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.152-158
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• 2003

A machine-vision guided inspection system with go-no gauges for inspecting eggcrate assemblies in steam generators is considered. To locate the gauge at the right place, periodic corrective actions for its position and traveling direction are required. We present a machine vision algorithm for determining inserting position and traveling direction of the go-no gauge. The overall procedure of the algorithm is composed of camera calibration, eggcrate image preprocessing, grid-height adjustment, intersection point estimation between two intersecting grids, and adjustment of position and traveling direction of the gauge. The intersection point estimation is performed by using linear regression with a constraint. A test with a real eggcrate specimen shows the feasibility of the algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1663-1667
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• 2003

This thesis is on tile development of LD(Laser Diode) chip tester and the control system based on graphical programming language(LabVIEW) to control the equipment. The LD chip tester is used to test the optic power and the optic spectrum of the LD Chip. The emitter size of LD chip and the diameter of the receiver(optic fiber) are very small. Therefore, in order to test each chip precisely, this tester needs high accuracy. However each motion part of the tester could not accomplish hish accuracy due to the limit of the mechanical performance. Hence. an image processing with machine vision was carried out in order to compensate for the error. and also a load test was carried out so as to reduce tile impact of load on chip while the probing motion device is working. The obtained results were within ${\pm}$5$\mu\textrm{m}$ error.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.209-212
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• 2018

Anisotropic conductive film(ACF) bonding is widely used for making fine interconnections between two different materials where soldering is not easily applicable. There are three constraints for the successful implementation of ACF bonding. A bonding contact should be pressed by a hot head with the right pressure and temperature for a pre-defined curing time. In this paper, a method for ACF bonding based on machine vision system is proposed and verified through some experiments. The system calculates the position and orientation of printed circuit boards(PCBs) on a bonding table and estimates the optimal hitting point where the hot head should be applied. Experimental results show that the proposed system achieves better adhesive strength by providing head flatness over contact surfaces.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.872-898
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• 2007

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection due to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.70-75
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• 2016

Nowadays, there are many research for recognizing face of people using the machine vision. the machine vision is classification and analysis technology using machine that has sight such as human eyes. In this paper, we propose algorithm for classifying human face using this machine vision system. This algorithm consist of Convolutional Neural Network and cascade face detector. And using this algorithm, we classified the face of subjects. For training the face classification algorithm, 2,000, 3,000, and 4,000 images of each subject are used. Training iteration of Convolutional Neural Network had 10 and 20. Then we classified the images. In this paper, about 6,000 images was classified for effectiveness. And we implement the system that can classify the face of subjects in realtime using USB camera.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3416-3422
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• 2014

A shock absorber rod is located in the center of the absorber piston and is responsible for the reciprocating movement portion. If it has surface defects, the damping performance of product will be adversely affected. A rod surface has gloss by heat treatment. Therefore, it is difficult to find a defect, such as dust, imprints, and blowholes. Because a total inspection is achieved by visual inspection by workers, it causes eyestrain and the quality of the product is not constant. In this paper, a machine vision system was developed to find a defect using a line-scan camera. The machine can detect surface defects than 0.3mm. To minimize the occurrence probability of defects on the inspection process, the developed auto inspection system had an automatic feeding system and incorporated a protection system. Through the development of this system, which relies on the operator's visual inspection of the surface of the shock absorber, the Rod inspection system constructed quality inspection standards and standardized tests to ensure improved reliability.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.184-186
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• 2004

This paper is about vision system that exhibits automatic examination of the conditions of shaft's worm. The system is composed of three part : image acquisition, vision algorithm, and user interface. The image acquisition part is composed of motor control, illumination and optics. The vision algorithm examines the parts by labeling algorithm using shaft image. User interface is divided into two parts, user interface for feature registering with control value settings and user interface for examination operation. The automatic inspection system of this research is a tool for final examination of shaft worm. This tool can be practically used in production lines with simple adjustments.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.903-904
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• 2006

This paper is about a vision system that exhibits automatic examination of the conditions of shaft's worm. The system is composed of three part : image acquisition, vision algorithm, and user interface. The image acquisition part is composed of motor control, illumination and optics. The vision algorithm examines the parts using shaft image. User interface is divided into two parts, user interface for feature registering with control value settings and user interface for examination operation. The automatic inspection system introduced in this paper can be used as a tool for final examination of shaft worm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.255-258
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• 1995

Surface profile is an important paramerer to evaluate accuracy of machined worpiece. It is necessary to acquire this data by in-process measurement. Recent researchers have introduced Machine Vision technique to achieve it. But it is difficult to apply it to industry field yet. In this study, in-process measuring system of surface profile is developed using CCD camera. The effect of illuminance according to incident angle is investigated and surface profile from surface tester and illuminance graph are compared experimentally.

• Journal of Biosystems Engineering
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• v.24 no.1
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• pp.59-66
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• 1999

Weed identification is important for precision farming. A machine vision system was applied to detect weeds. Shape features were analyzed with the binary images obtained from color images of radish, purslane, goosefoot, and crabgrass. Features studied were aspect, roundness, compactness, elongation, PTB, LTP, LTW, and PTAL of each plant. Discriminant analysis was used to classify plant species. The best shape features that distinguished crabgrass were LTP and LTW which distinguished the crabgrass from the others with 100%. Two dimensional discrimination by using LTP and PTB appeared to be effective for distinguishing radish, purslane, and goosefoot.