• Journal of Digital Convergence
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• v.16 no.3
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• pp.303-309
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• 2018

Computer vision technology is a component inspection to obtain a video image from the camera to the machine to perform the capabilities of the human eye with a field of artificial intelligence, and then analyzed by the algorithm to determine to determine the good and bad of production parts It is widely applied. Shape inspection method was used as how to identify the location of the start point and the end point of the search range, measure the height to the line scan method, in such a manner as to determine the presence or absence of the bolt tabs average brightness of the inspection area in a circular scan type value And the degree of similarity was calculated. The total time it takes to test in the test performance tests of two types of bolts tab enables test 300 min, and demonstrated the accuracy and efficiency of the inspection on the production line represented a complete inspection accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.826-828
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• 2014

Recently trend of product is miniaturization. As a result, We need products surface as well as products internal defect inspection. Generally, Inspection products in production process uses a lot of optical inspection. However, This is difficult to internal inspection of products. We used optical device instead of X-ray generator. At the same time, We have developed system to determine the product defect. First, obtain X-ray image from Machine vision function. Next, Measured value is recognize suitability within error range. otherwise recognize defect. Results presence of defective products can be stored by user.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.405-406
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• 2010

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.247-252
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• 2004

Developed shape awareness technology and vision technology for optical ten slant in this research and including external form state of lens for the performance verification developed so that can be good achieve badness finding. And, establish to existing reflex data because inputting surface badness degree of scratch's standard specification condition directly, and error designed to distinguish from product more than schedule error to badness product by normalcy product within schedule extent after calculate the error comparing actuality measurement reflex data md standard reflex data mutually. Developed system to smallest 1pixel unit though measuring is possible 1pixel as 3.7$\mu\textrm{m}$${\times}$3.7$\mu\textrm{m}$(0.1369${\times}$10/sub-1/$\textrm{mm}^2$) the accuracy to 10/sub-1/mm minutely measuring is possible performance verification and trust ability through an experiment prove.

• Journal of the Semiconductor & Display Technology
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• v.10 no.1
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• pp.113-118
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• 2011

This paper proposes computer visual inspection algorithms for PCB defects which are found in a manufacturing process. The proposed method can detect open circuit and short circuit on bare PCB without using any reference images. It performs adaptive threshold processing for the ROI (Region of Interest) of a target image, median filtering to remove noises, and then analyzes connected components of the binary image. In this paper, the connected components of circuit pattern are defined as 6 types. The proposed method classifies the connected components of the target image into 6 types, and determines an unclassified component as a defect of the circuit. The analysis of the original target image detects open circuits, while the analysis of the complement image finds short circuits. The machine vision inspection system is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiment results show that the proposed algorithms are quite successful.

• Korean Journal of Optics and Photonics
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• v.20 no.4
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• pp.223-229
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• 2009

This paper describes the development of a simulator which can numerically calculate an image to be acquired in a machine vision system for automated optical inspection. The simulator is based on a ray tracing technique and composed of three modules which are an illuminating system, a specimen and an imaging system. Kinds of model parameters for modules and their values are carefully chosen from the direct measurement and the observation of related phenomena. Finally, the validity of the simulator is evaluated by logical analysis and by comparison with measured images.

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.179-189
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• 2007

Most of the seeds of cucurbitaceous rootstock species used for grafting were mainly sown by hand. This study was carried out to develop an on-line discriminating algorithm of seed direction using machine vision and an automatic seeding system. The seeding system was composed of a supplying device, feeding device, machine vision system, reversing device, seeding device and system control section. Machine vision was composed of a color CCD camera, frame grabber, image inspection chamber, lighting and personal computer. The seed image was segmented into a region of seed part and background part using thresholding technique in which H value of HSI color coordinate system. A seed direction was discriminated by comparing position between the center of circumscribed rectangle to a seed and the center of seed image. It took about 49ms to identify and redirect seed. Line-up status of seed was good the more than 95% of a sowed seed. Seeding capacity of this system was shown to be 10,140 grains per hour, which is three times faster than that of a typical worker.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1339-1347
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• 1997

The automation of final inspection and tuning process in the manufacturing of electric products is hot issue now, because it is the only part that has not been wholey automized yet, mainly due to the difficulties to handle so small size of VR which is the final tuning point in the most of electric products. For the automation of this process, at first the accurate measurement of position and orientation of SMD VR on PCB in real time is strongly needed. In this paper, a new image processing algorithm to detect the center position and orientation of target VR by using machine vision is proposed for automatic final tuning of the 8mm camcoder's performance. In the method, the outline feature of object is used actively. The usefulness of the proposed methods were tested by several experiments, and the results showed enough accuracy for both of position and orientation. Additatively, we discussed about the total visual system construction and preprocessing of image.

• Journal of IKEEE
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• v.25 no.3
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• pp.485-492
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• 2021

In this paper, we propose the development of a defect inspection system for polygonal containers. Embedded board consists of main part, communication part, input/output part, etc. The main unit is a main arithmetic unit, and the operating system that drives the embedded board is ported to control input/output for external communication, sensors and control. The input/output unit converts the electrical signals of the sensors installed in the field into digital and transmits them to the main module and plays the role of controlling the external stepper motor. The communication unit performs a role of setting an image capturing camera trigger and driving setting of the control device. The input/output unit converts the electrical signals of the control switches and sensors into digital and transmits them to the main module. In the input circuit for receiving the pulse input related to the operation mode, etc., a photocoupler is designed for each input port in order to minimize the interference of external noise. In order to objectively evaluate the accuracy of the development of the proposed polygonal container defect inspection system, comparison with other machine vision inspection systems is required, but it is impossible because there is currently no machine vision inspection system for polygonal containers. Therefore, by measuring the operation timing with an oscilloscope, it was confirmed that waveforms such as Test Time, One Angle Pulse Value, One Pulse Time, Camera Trigger Pulse, and BLU brightness control were accurately output.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.2
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• pp.92-99
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• 2007

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding systems, is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. In this paper, novel presented, developed vision processing techniques are detailed, and their application in welding fabrication is covered. The software for joint tracking system is finally proposed.