• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.601-603
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• 2003

최근에 차량수의 증가로 인하여 교통량이 증가하고 그로 인하여 ITS(Intelligent Transport System)에 대한 관심이 증가하게 되었다. 그 중에서도 LPR system(License Plate Recognition system)은 ITS에서 중요한 역할을 한다. 본 논문에서는 차량의 번호를 인식하기 위해 선행되어야 하는 과정인 대상 차량의 번호판 영역을 추출하고 구성 숫자들을 분리하는 알고리즘을 제안한다. 이 알고리즘은 영상에서 차량의 번호판 영역을 찾는 부분과 번호판의 숫자를 분리하는 부분으로 구성이 되어 있다. 먼저 입력 영상에서 gradient를 구하게 된다. 구해진 gradient에서 차량의 구조와 transition의 횟수를 조사를 통해서 번호판 영역을 찾게 된다. 찾아진 번호판 영역에서 adaptive threshold를 적용하여 숫자들을 분리하게 된다. 실내 주차장 환경에서 촬영된 영상을 대상으로 실험을 수행하고 그 결과를 정리하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.138-141
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• 2014

본 논문에서는 색상 정보를 이용하여 배경 영역이 포함된 자동차의 전,후면 사진에서의 자동차 번호판 영역(녹색, 흰색) 추출과 추출된 번호판에서 글자를 분리해내는 방법을 제안한다. 기존의 색상 정보를 이용하여 번호판을 추출하는 방법은 흰색 번호판(신형 번호판)의 경우에는 배경 영역에서 흰색인 영역도 많고 국내 차량 중에 흰색 차량이 많기 때문에 번호판 영역과 배경 영역 사이의 명확한 구분에 어려움이 있었다. 따라서 행별 Red값 변화도를 조사하여 배경 영역과 번호판 영역 사이의 명확한 구분을 하게 하며, 흰색 번호판의 경우에 추출이 안되면 흰색의 기준을 더 낮추어서 다시 영역 추출을 할 수 있는 재추출 알고리즘을 추가해서 비교적 어두운 사진에서도 번호판영역을 추출할 수 있도록 한다. 추출된 번호판에서 글자를 추출해내는 과정에서도 이진화를 거치면 노이즈가 많이 생기기 때문에 이를 줄이고자 행별 Red값 변화도를 조사하여 번호판 영역에서 위아래 부분의 노이즈를 줄일 수 있도록 하였다.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.145-151
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• 2013

This research is to suggest a method for investigating car number plates among the information managed in parking facilities. The investigation of car number plate is generally used to know how long vehicles are parked. Also, it can provide the information about the parking turnover rate and the mean parking duration of parked vehicles. This research performs the investigation using cameras at a distance of time. That is, the given distance of time from cameras is assigned to each parked vehicle, and then it can find the mean parking time of parked vehicles. Also, it can check the parking turnover rate of parked vehicles at a space unit of parking lot in an hour. The information such as the mean parked duration and the parking turnover rate of parked vehicles taken from this method is helpful to find and understand the inefficient use of parking facilities. With this suggested method, this research attempted to check the mean parking duration and the parking turnover rate of parked vehicles.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.109-110
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• 2016

본 논문에서는 효율적으로 주차 공간을 확보와 주차장 성능을 향상을 위한 방법을 조사하였다. 이러한 방법으로는 차량 번호판 조사를 이용하여 주차 회전율을 구하는 방법이 있었다. 본 연구로 효율적으로 주차장을 사용하고 있는지를 판단할 수 있다. 또한 차량의 주차를 하여 잘 소통되는지를 알 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.205-206
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• 2017

본 논문에서는 주차 시설 이용 현황 중에서 차량 번호판 조사를 통하여 평균주차시간과 주차 회전율을 구하도록 한다. 관찰하는 사람이 없이 카메라를 활용하고, 조사 시간에 일정한 간격을 주어 조사하도록 한다. 일정한 조사 시간 간격을 주차된 차량에 나눠주어 평균 주차 지속 시간을 구하도록 한다. 그리고 이렇게 하여 주차면 1개당 1시간당 주차 차량 대수인 주차 회전율을 구하도록 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2645-2650
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• 2014

This paper researches on securing parking spaces and improving parking performance, by use of vehicle license plate investigation for average parking time and parking turnover ratio. Even though general surveys on vehicle license plate investigation are performed by the person, our survey is performed by the machine, CCTV. Unusually, our method checks average parking time and parking turnover ratio at time intervals of the survey. So, it is easy to check whether a parking lot is effectively used and its traffic flows smoothly or not. In our experiment, we exclude a method to recognize characters by use of 4-direction projection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1352-1357
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• 2002

This Paper describes algorithm to extract license plates in vehicle images. Conventional methods perform preprocessing on the entire vehicle image to produce the edge image and binarize it. Hough transform is applied to the binary image to find horizontal and vertical lines, and the license plate area is extracted using the characteristics of license plates. Problems with this approach are that real-time processing is not feasible due to long processing time and that the license plate area is not extracted when lighting is irregular such as at night or when the plate boundary does not show up in the image. This research uses the gray level transition characteristics of license plates to verify the digit area by examining the digit width and the level difference between the background area the digit area, and then extracts the plate area by testing the distance between the verified digits. This research solves the problem of failure in extracting the license plates due to degraded plate boundary as in the conventional methods and resolves the problem of the time requirement by processing the real time such that practical application is possible. This paper Presents a power automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates wan 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.29-35
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• 2009

In this paper by using the image taken from the rear of the vehicle to effectively extract the license plate and how to recognize the characters appearing in the offer. How to existing research on the entire video by following the pre-edge (edge) images to obtain yijinhwa. Qualified heopeu in a binary image (Hough) to convert the horizontal and vertical lines to obtain, using the characteristics of the plates to extract the license plate area. The problem with this method, the processing time is so difficult to handle real-time status of irregular points, and visual contrast with yagangwan border does not appear in the plates to extract the license plate area is that it is not. In addition, the rear of the vehicle license plate area from images taken using the characteristics of the plates myeongamgap changes sutjapok in the area, background area and the number number area of the region confirmed the contrast of the car and identified the number and the number of 42 of distance to extract the license plate area. How to research, the existing damage to the border of the plate to fail to extract the license plate area, a matter of hours to resolve problems in real-time, practical application is processed. Chapter 100 as the results of the experiment the sample video image in a car that far experiment results automatically read license plates have been able to extract the license plate and failing to represent 13% of images, character recognition result of failing to represent the image was 0.4%

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1041-1042
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• 2015

This paper studies on securing parking spaces and improving parking performance, by use of vehicle license plate investigation for parking turnover ratio. It is easy to test whether a parking lot is effectively used and its traffic flows smoothly or not.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.1
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• pp.73-80
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• 2016

The Car License Plate Recognition(: CLPR) is required in searching the hit-and-run car, measuring the traffic density, investigating the traffic accidents as well as in pursuing vehicle crimes according to the increasing in number of vehicles. The captured images on the real environment of the CLPR is contaminated not only by snow and rain, illumination changes, but also by the geometrical distortion due to the pose changes between camera and car at the moment of image capturing. We propose homographic transformation and intensity histogram of vertical image projection so as to transform the distorted input to the original image and cluster the character and number, respectively. Especially, in this paper, the Multilayer Perceptron Algorithm(: MLP) in the CLPR is used to not only recognize the charcters and car license plate, but also determine the optimized ratio among the number of input, hidden and output layers by the real experimental result.