• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.445-446
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• 2007

The goal of this paper is accuracy improvement of appearance inspection by using image separation average value matching method. The appearance inspection of various electronic products and parts has been executed by the eyesight of human. But inspection by eyesight can't bring about uniform inspection result. Because the appearance inspection result by eyesight of human is changed by condition of physical and spirit of the checker. So machine vision inspection system is currently used to many appearance inspection fields instead of the checker. However the inspection result of machine vision is changed by the illumination of workplace. Therefore we have used a average value matching in this paper for improvement of vision inspection accuracy and could increase inspection accuracy of vision system. In other words, we made an effort for elevation of vision inspection accuracy by making the identical image separation average value of reference image and input image. Also this system has been developed only using PC, CCD Camera and Visual C++ for universal workplace.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.1
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• pp.22-29
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• 2008

This paper describes the developed results of vision system for the recognition of material management characters, which was included in the billet image. The material management characters, which was marked at the surface of billet, should be recognized before billet moves to the next process. Our vision system for the character recognition includes that CCD camera system which acquire billet image, optical transmission system which transmit captured image to the long distance, input and output system for the interface with existing system and software for the character recognition. We have installed our vision system at the wire rod line of steel & iron plant and tested. Also, we have performed inspection of durability, reliability and recognition rate. Through the testing, we have confirmed that our system have high recognition rate, 98.6%.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08b
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• pp.1478-1481
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• 2007

This paper introduced a measurement method for image sticking based on human vision perception. Existing image sticking quantification method is mostly different from visible level by human perception. It takes a long time to measure image sticking which is degraded by time due to using a spot photometer, therefore many test samples could not be evaluated in a given short period of time in mass production line. However, the new measurement method in this paper is possible to evaluate a large quantity of samples in fast and high correlation with human perceptual level of image sticking.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.4
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• pp.240-250
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• 1988

This pc-vision system digitizes/displays 512*512*8 bit pixel image in real time and is capable of the various image processing. This system provides a versatile solution to those users pursuing high performance image processing system compatible with the VME bus, and is general purpose imaging system giving the optimal efficiency for machine vision, medical use and various task. In this paper, Image processing technique has classified image enhancement and image analysis in order to design and implement the pc-vision system. In order to improve processing speed, This system unilizing ROI processing performs point operation, local operation and global operation as well as common arithmetic/logic operation in real time.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.446-452
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• 2008

Machine vision system was used for analyzing leaf color disorders of tomato plants in a greenhouse. From the day when a few leave of tomato plants had started to wither, a series of images were captured by 4 times during 14 days. Among several color image spaces, Saturation frame in HSI color space was adequate to eliminate a background and Hue frame was good to detect infected disease area and tomato fruits. The processed image ($G{\sqcup}b^*$ image) by OR operation between G frame in RGB color space and $b^*$ frame in $La^*b^*$ color space was useful for image segmentation of a plant canopy area. This study calculated a ratio of the infected area to the plant canopy and manually analyzed leaf color disorders through an image segmentation for Hue frame of a tomato plant image. For automatically analyzing plant leave disease, this study selected twenty-seven color patches on the calibration bars as the corresponding to leaf color disorders. These selected color patches could represent 97% of the infected area analyzed by the manual method. Using only ten color patches among twenty-seven ones could represent over 85% of the infected area. This paper showed a proposed machine vision system may be effective for evaluating various leaf color disorders of plants growing in a greenhouse.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.161-164
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• 2006

In this thesis we designed the vision system to inspect the defect of a back light unit of plat panel display device. The vision system is divided into hardware and inspection algorithm of defect. Hardware components consist of illumination part, robot-arm controller part and image-acquisition part. Illumination part is made of acrylic panel for light diffusion and five 36W FPL's(Fluorescent Parallel Lamp) and electronic ballast with low frequency harmonics. The CCD(Charge-Coupled Device) camera of image-acquisition part is able to acquire the bright image by the light coming from lamp. The image-acquisition part is composed of CCD camera and frame grabber. The robot-arm controller part has a role to let the CCD camera move to the desired position. To take inspections of surface images of a flat panel display it can be controlled and located every nook and comer. Images obtained by robot-arm and image-acquisition board are saved on the hard-disk through windows programming and are tested whether there are defects by using the image processing algorithms.

• Journal of International Society for Simulation Surgery
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• v.3 no.2
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• pp.39-45
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• 2016

The augmented reality is the environment which consists of real-world view and information drawn by computer. Since the image which user can see through augmented reality device is a synthetic image composed by real-view and virtual image, it is important to make the virtual image generated by computer well harmonized with real-view image. In this paper, we present reviews of several works about computer vision and graphics methods which give user realistic augmented reality experience. To generate visually harmonized synthetic image which consists of a real and a virtual image, 3D geometry and environmental information such as lighting or material surface reflectivity should be known by the computer. There are lots of computer vision methods which aim to estimate those. We introduce some of the approaches related to acquiring geometric information, lighting environment and material surface properties using monocular or multi-view images. We expect that this paper gives reader's intuition of the computer vision methods for providing a realistic augmented reality experience.

• Journal of Biosystems Engineering
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• v.25 no.5
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• pp.409-414
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• 2000

A machine vision system was built to obtain single stationary image from an egg. This system includes a CCD camera, an image processing board and a lighting system. A computer program was written to acquire, enhance and get histogram from an image. To minimize the evaluation time, the artificial neural network with the histogram of the image was used for eggshell evaluation. Various artificial neural networks with different parameters were trained and tested. The best network(64-50-1 and 128-10-1) showed an accuracy of 87.5% in evaluating eggshell. The comparison test for the elapsed processing time per an egg spent by this method(image processing and artificial neural network) and by the processing time per an egg spent by this method(image processing and artificial neural network) and by the previous method(image processing only) revealed that it was reduced to about a half(5.5s from 10.6s) in case of cracked eggs and was reduced to about one-fifth(5.5s from 21.1s) in case of normal eggs. This indicates that a fast eggshell evaluation system can be developed by using machine vision and artificial neural network.

• Journal of the Korean Institute of Navigation
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• v.16 no.3
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• pp.77-85
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• 1992

Camera arrangement for depth and image correspondence is very important to the computer vision. Two conventional comera arrangements for stereo computer vision are lateral model and axial motion model. In this paper, using the axial motion stereo camera model, the algorithm for camera focal length measurement and the surface smoothness with the radiance-irradiance is proposed fro 3-dimensional image correspondence on stereo computer vision. By adapting the above algorithm, camera focal length can be measured precisely and the resolution of 3-dimensional image correspondence has been improved comparing to that of the axial motion model without the radiance-irradiance relation.

• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.782-788
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• 2000

In this paper, we present a new approach to visual feedback control using image-based visual servoing with stereo vision. In order to control the position and orientation of a robot with respect to an object, a new technique is proposed using binocular stereo vision. The stereo vision enables us to calculate an exact image Jacobian not only around a desired location but also at other locations. The suggested technique can guide a robot manipulator to the desired location without providing a priori knowledge such as the relative distance to the desired location or the model of an object even when the initial positioning error is large. This paper describes a model of stereo vision and how to generate feedback commands. The performance of the proposed visual servoing system is illustrated by experimental results and compared with conventional control methods for an assembly robot.