• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.423-426
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• 1995

An image processing algorithm is developed in order to recognize the type of cars, the position of a number plate and the characters on the plate. To recognize the type af cars, comparison of two images is used. One has a car image, the other is just a background image without car. After that recognition, a vertical line filter is used to find the location of the plate. Finally the similarity method is used to recognize the numbers on the plates.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.219-219
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• 2000

A Plate type pipe is used for heat exchange in radiator of a vehicle. The pipe has several rooms through which water flows and heat is dissipated into outside . In the case that there are small holes, cracks or some scratches on the plate, the radiators lost their functions due to Leakage. This may result in overheating of engine in a car. Thus, we need to perform the entire inspection of the plate type pipe in advance before assembling the radiator. In manufacturing process of the plate type pipe, the productive speed is very high and that may be performed continuously. So, there is no room to perform the outlook inspection by typical image processing techniques. This paper proposes a new method to inspect the outlook surface of the plate type pipe automatically with high speed. Especially, the sequential processing technique of an algorithm which detects defects on the surfaces of the plate type pipe is proposed for line scan camera which captures line image. To evaluate the inspection performance, a series of experiments is performed.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.41-44
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• 1996

There are studied for filmness methods due to disadvantage of conventional X-ray system. Have high DQE, high SNR, amorphous selenium was investigated with image plate. In this point, characteristics of all amorphous selenium image plate was investigated to understand the relationship between the amount of the X-ray exposure and the superficial charge potential in this study. Specially, changes in charge at the surface of the amorphous selenium plate with respect to change of X-ray energy(KeV) was investigated. It was found that the surface charge potential at the amorphous selenium inclosed with respect to the increse in X-ray exposure and that the changed surface potential was a semilinearity in 12.5KeV - 22.5KeV range. Therefore, which suggests that these results call be applied to the development of X-ray image plate.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.380-383
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• 2003

The purpose of this study is to develope prototype EPID system and improve image quality of radiation therapy field imaging system using CCD camera. For this research we used Linac(Clinac 4/100), Copper metal plate, $Gd_2O_2S_2$ phosphor and CCD camera(Photronic). In this study we find best thickness of buil-up metal plate and acquired projection image of humanoid head phantom. Also we enhanced raw image data using superposition and histogram stretching method. Through the thickness optimized of metal plate and image processing, we confirmed of an improved image quality of an EPID system using CCD camera. As result, highest quality image was acquired at 1mm thickness of Copper metal plate and improved image quality by image processing methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1114-1125
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• 2013

License Plate Detection (LPD) is a key component in automatic license plate recognition system. Despite the success of License Plate Recognition (LPR) methods in the past decades, the problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper aims at automatical detection of car license plates via image processing techniques. In this paper, we proposed a real-time and robust method for license plate detection using Heuristic Energy Map(HEM). In the vehicle image, the region of license plate contains many components or edges. We obtain the edge energy values of an image by using the box filter and search for the license plate region with high energy values. Using this energy value information or Heuristic Energy Map(HEM), we can easily detect the license plate region from vehicle image with a very high possibilities. The proposed method consists two main steps: Region of Interest (ROI) Detection and License Plate Detection. This method has better performance in speed and accuracy than the most of existing methods used for license plate detection. The proposed method can detect a license plate within 130 milliseconds and its detection rate is 99.2% on a 3.10-GHz Intel Core i3-2100(with 4.00 GB of RAM) personal computer.

• Journal of the Korean Graphic Arts Communication Society
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• v.26 no.1
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• pp.73-85
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• 2008

We have manufactured a photoresist which has excellent dispersity and good applying property due to 330cps of viscosity for environment-friendly and economical maskless screen plate making. And the photoresist applied on the screen stretched was exposed without mask by beam projector with CRT light source. Then it was developed by air spray with $1.7kgf/cm^2$ of injection pressure. The pencil hardness and solvent resistance of curing photoresist film were worse than those of conventional photoresist film and the maximum resolution of line image formed by maskless screen plate making was 0.5 mm since the exposure system for maskless plate making has weak light intensity and the diffusion of light. But we could obtain maskless screen plate which has sharp edges of line image and confirm a possibility of dry development process by air spray method.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1172-1179
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• 2015

A wireless license plate region extracting module is proposed for LPR system controlling multiple gates. This module is cheaply implemented using Raspberry Pi which is open source and high performance. First, as the upper 1/3 of the captured image is discarded as it has no useful information on license plate. Using the OpenCV libraries the edge image is got by Canny algorithm after applying Gaussian filtering to gray image, and the labeling is conducted for 4 consecutive numbers in license plate. These numbers are located using various decision equations, and expanding the numbers region the final license plate region can be extracted. The result image is transferred to Server using wifi direct. Using the proposed module it becomes easy to set up and maintain the LPR system. The experimental results showed that the successful extracting rate was 98.4% using 500 car images with 640 × 480 resolution.

• Korean Journal of Digital Imaging in Medicine
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• v.13 no.4
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• pp.145-151
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• 2011

The purpose of this study is to know some changes of resolution and image if we remove scattered ray using lead plate when doing lumbar lateral projection. Using 3 DR system(2 FD types, 1 CCD type) equipments and 2 film system equipments, we gain the image whether the phantom of abdomen equivalent sticking resolution chart has lead plate or not, whether we do collimation or not. Also, we use ion chamber, measure radiation exposure rate and change to entrance surface dose from it. we gain that images in the greatest condition of taking in clinic. 5 people in this group decoded resolution with our eyes, measured thickness of images and compared them from each equiments. Resolution has difference to size of collimation in DR FD type. Also there is no difference the original image with the new image which we abbreviated mAs. In DR CCD type, resolution didn't have difference whether lead plate is or not and whether we do collimation or not. In film type, existing or nonexisting of lead plate didn't influence on resolution. Lead plate makes the quality of image higher due to reducing scattered ray, it doesn't influence on resolution.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.718-723
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• 2014

In this paper, we developed the inspection system of electric vehicle battery plate using image processing. Four cameras are used for acquiring the principal parts of the plate, and several steps of image processing for extracting significant dimensions of the plate such as widths and lengths. As a preceding step, calibration of four cameras is carried for compensating distorted images using dot-arrayed sheet. Coordinate systems for four cameras are defined where one coordinate system is assigned to the reference coordinate system to which the others are relatively described. Line information of the edge in the windowed image is extracted using elaborate edge-detection algorithm, and finally the intersection points between lines are extracted to calculate widths and lengths of the plate from which the error status of the battery plate is decided.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3490-3507
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• 2014

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.