• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.1
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• pp.59-67
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• 2008

In this paper we propose an algorithm to extract the license plate regions from Korean car images. The idea of this paper is that we first find the four digits in the input car image and then segment the plate region using the digit information. Out method has advantage of segmenting simultaneously the plate regions and four digits regions. The first step finds and groups the connected components with proper sizes as candidate digits. The second step applies an serial alignment condition to find out probable 4-digits. In the third step, we recognize the candidate digits and assign the confidence values to each of them. The final step extracts the license plate region which has the highest confidence value. We used the Perfect Metrics classification algorithm to estimate the confidence. In our experiment, we got 97.23% and 95.45% correct detection rates, 0.09% and 0.11% false detection rates for 4,600 daytime and 264 nighttime images, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.131-141
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• 2012

This paper proposes a method of analyzing unrecognizable numbers of license plate images, which are degraded by various factors such as low resolution, low light level, geometric distortion, and periodic noise, to name a few. With existing vehicle license plate recognition methods, it is difficult to recognize license plate if images are not recognizable in the pre-process of removing degradation factors. Although images of license plate have not been improved to be recognizable in the pre-process, the proposed method makes it possible to recognize numbers of license by distorting pre-saved reference images of license plate numbers same as sample plates, and by assuming likelihood ratio using statistical methods. The proposed method also makes it possible to identify suspect vehicle license plate under unstable light conditions and with low resolution images that are unrecognizable by the naked eye. This method has been used in real criminal investigation to recognize numbers of license plate of criminal vehicle, and has proved to be useful as criminal evidence through experiments under various conditions.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.257-264
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• 2005

The number of vehicles are rapidly increased as our society is developed. The vehicle recognition has been studied for a while because many people acknowledged it has critical functions to solve the problems of traffic control or vehicle-related crimes. In this paper a novel method is proposed to recognize vehicle models corresponding makers. Vehicles' models are recognized based on the texture parameters from segmented radiator region above a number plate. A three-layer neural network was built and trained with the texture features for recognition. The proposed method shows $93.7\%$ of recognition rate and $99.7\%$ of specificity for vehicles' model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.157-161
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• 2017

This paper introduce UHF miniaturized RFID tag antenna which is embedded on the seal-bolt or plastic bolt for automobile plate. To detect the illegal and un-registered car, the illegal automobile detection system has been developed using the seal-bolt UHF RIF tag antenna. The diameter of seal-bolt UHF tag is about 24mm, almost the same size as 100 Won coin. The simulated and measured reflection coefficient are compared, and the reading range patten is also measured. If seal-bolt tag is embedded on car plate, police can get information of automobile and detect illegal vehicles easily with the illegal automobile detection system.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.409-415
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• 2003

Recently, the modular learning are very popular and receive much attention for pattern classification. The modular learning method based on the "divide and conquer" strategy can not only solve the complex problems, but also reach a better result than a single classifier′s on the learning quality and speed. In the neural network area, some researches that take the modular learning approach also have been made to improve classification performance. In this paper, we propose a simple modular neural network for characters recognition of vehicle number plate and evaluate its performance on the clustering methods of feature vectors used in constructing subnetworks. We implement two clustering method, one is grouping similar feature vectors by K-means clustering algorithm, the other grouping unsimilar feature vectors by our proposed algorithm. The experiment result shows that our algorithm achieves much better performance.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.6-11
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• 2018

Recently, many drivers of two-wheeled motorcycles in leisure and express delivery industries etc. brought increasing issues for the motorcycle safety. This paper has studied the structural safety of two-wheeled motorcycles considering the regulations for stand, rear registration plate and anti-tampering measures. The authors have investigated and compared a variety of international and domestic regulations and specifications for structural safety of those components. Detailed experimental procedures of stand, mounting for rear registration plate and anti-tampering measures are proposed to secure the two-wheeled motorcycle safety and the outcomes of this study can be used to confirm the Korean motor vehicle safety standards.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.239-240
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• 2017

에너지 신산업 8대 산업 중 핵심 산업인 전기자동차에 대하여 전문가들의 2025년 전기자동차 600조원의 시장 형성 예측을 하고 있고 2017년 국토부는 전기자동차 전용 번호판 도입을 추진하고 있다. 이에 따른 전기자동차 수요와 공급 증가에 따른 사용자들의 증가를 예측하고 사용자 요구사항을 충족시켜줄 수 있는 홈페이지를 미리구축함으로 충전소의 길 찾기를 좀 더 쉽게 알 수 있기를 위하여 해당 프로젝트를 진행하게 되었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.152-155
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• 2019

클라우드가 대중화되어 많은 모바일 유저들이 자동 백업 기능을 사용하면서 민감한 개인정보가 포함된 사진들이 무분별하게 클라우드에 업로드 되고 있다. 개인정보를 포함한 클라우드가 악의적으로 해킹 될 시, 사진에 포함된 지문, 자동차 번호판, 카드 번호 등이 유출됨에 따라 대량의 개인정보가 유출될 가능성이 크다. 이에 따라 적절한 기준에 맞게 사진 속 개인 정보 유출을 막을 수 있는 기술의 필요성이 대두되고 있다. 현재의 클라우드 시스템의 문제를 해결하고자 본 연구는 모바일 기기에서 클라우드 서버로 사진을 백업하는 과정에서 영역 검출과 블러링의 과정을 제안하고 있다. 클라우드 업로드 과정에서 사진 속의 개인 정보를 검출한 뒤 이를 블러링하여 클라우드에 저장함으로써 악의적인 접근이 행해지더라도 개인정보의 유출을 방지할 수 있다. 머신러닝과 computer vision library등을 이용하여 이미지 내에 민감한 정보를 포함하고 있는 영역을 학습된 모델을 통해 검출한 뒤, OpenCV를 이용하여 블러링처리를 진행한다 사진 속에 포함될 수 있는 생체정보인 지문은 손 영역을 검출한 뒤, 해당 영역을 블러링을 하여 업로드하고 카드번호나 자동차 번호판이 포함된 사진은 영역을 블러링한 뒤, 암호화하여 업로드 된다. 후에 필요에 따라 본인인증을 거친 후 일정기간 열람을 허용하지만 사용되지 않을 경우 삭제되도록 한다. 개인정보 유출로 인한 피해가 꾸준히 증가하고 있는 지금, 사진 속의 개인 정보를 보호하는 기술은 안전한 통신과 더불어 클라우드의 사용을 더 편리하게 할 수 있을 것으로 기대된다.

• Journal of the Ergonomics Society of Korea
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• v.11 no.1
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• pp.13-19
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• 1992

This paper deals with the legibility of Korean characters(Hangeul) on car number licence plates. We focus of the height-width ratio as key factor affecting the legibility in experimental procedures. The results of experiments are; i) For vertica characters, the vest height-width ratio was 1:1 for 3 throuth 5 stroke characters, and 4:5 for 6 and 7 stroke characters. ii) For horizontal characters, the best height-width ratio was 1:1 for 3 through 5 stroke charcters, and 5:4 for 6 and 7 stoke characters.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.352-360
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• 2002

본 논문에서는 미세 거리변와에 따른 자기자화의 변화를 구현하는데 있어 비선형적인 요소를 포함하고 있어 이를 수학적으로 모델링하여 제어기를 설계하는데는 많은 난점을 내포하고 있다. 따라서 거리변화에 따른 자기장의 비선형적인 변화 관계를 신경회로망 제어기의 학습을 통하여 제어할 수 있도록 신경 회로망 제어기를 제안하려 한다.