• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.352-352
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• 2004

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1081-1084
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• 1999

This paper proposes a method of extracting automobile license plate information using color image processing and separation of character regions. The hue and saturation of color information is need for license plate extraction and the specified standard location ratio is need for character region separation. Simulation results show that the proposed algorithm can detect license plates and separate character regions successfully.

• Digital Contents
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• no.8 s.159
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• pp.66-71
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• 2006

예전에 모뎀을 통한 PC통신이 한창 유행할 때, ROM족이라는 유행어가 있었다. RAM(Random Access Memory)과 함께 컴퓨터의 기억장치로 활용되는, 읽기만 가능한 저장매체(Read Only Memory)를 뜻하는 용어였다. 그런데 이 용어는 이 속성을 빗대어 게시판을 읽기만 하는 회원(Read Only Member)을 가리키는 말로도 쓰였다. 정보화시대에 있어서 정보라는 것, 비록 형태가 없는 것들이 대부분이지만 이 역시 누군가가 사용하기 위해서는 누군가가 만들어내야 한다. 수요와 공급이 일어나는 정보 유통 구조다. 혹자는 ROM족으로 소비생활에만 치중하지만, 또 다른 혹자들은 소비와 생산을 동시에 하는 프로슈머(ProSumer) 역할을 한다. 이번호에서는 이러한 생산성을 띤 소비자들에 대한 이야기를 하고자 한다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.309-314
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• 1987

Using computer picture processing, a method of extracting the region of a car number plate is described. A modified Hough transformation, in which parameter plane is restricted, is proposed. The demerits of Hough transformation, i.e., it requires much computation time and storage capacity, are reduced by this method. Further, taking the features of a car number plate into consideration, the region of a car number plate is extracted.

• Journal of the Korean Society for information Management
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• v.19 no.4
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• pp.53-76
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• 2002

This study compares and analyzes the classes in the major special areas in the sociology, called “branch sociology,” included in the Korean Decimal Classification 4th edition and Dewey Decimal Classification 21st edition. Especially it analyzes the related classes of specified areas (branch sociology) of sociology including those of arts and sports, sciences, languages, society, region, etc. class by class. In this analysis two systems show many differences in the classes included and in the locations of some classes. This analysis can be useful for the future revision of KDC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1248-1254
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• 2022

Efforts are being made to prevent traffic accidents in the school zone in advance. However, traffic accidents in school zones continue to occur. If the driver can know the situation information in the child protection area in advance, accidents can be reduced. In this paper, we design a camera that eliminates blind spots in school zones and a number recognition camera system that can collect pre-traffic information. It is designed by improving the LIDAR system that recognizes vehicle speed and pedestrians. It collects and processes pedestrian and vehicle image information recognized by cameras and LIDAR, and applies artificial intelligence time series analysis and artificial intelligence algorithms. The artificial intelligence traffic accident prevention system learned by deep learning proposed in this paper provides a forced push service that delivers school zone information to the driver to the mobile device in the vehicle before entering the school zone. In addition, school zone traffic information is provided as an alarm on the LED signboard.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.219-231
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• 2019

The specifications of signboards are set for each type of signboards, but the shape and size of the signboard actually installed are not uniform. In addition, because the colors of the signboard are not defined, so various colors are applied to the signboard. Methods for recognizing signboards can be thought of as similar methods of recognizing road signs and license plates, but due to the nature of the signboards, there are limitations in that the signboards can not be recognized in a way similar to road signs and license plates. In this study, we proposed a methodology for recognizing plate-type signboards, which are the main targets of illegal and old signboards, and automatically extracting areas of signboards, using the deep learning-based Faster R-CNN algorithm. The process of recognizing flat type signboards through signboard images captured by using smartphone cameras is divided into two sequences. First, the type of signboard was recognized using deep learning to recognize flat type signboards in various types of signboard images, and the result showed an accuracy of about 71%. Next, when the boundary recognition algorithm for the signboards was applied to recognize the boundary area of the flat type signboard, the boundary of flat type signboard was recognized with an accuracy of 85%.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1166-1174
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• 2017

In general, the detection of the vehicle license plate is a previous step of license plate recognition and has been actively studied for several decades. In this paper, we propose an algorithm to detect a license plate area of a moving vehicle from a video captured by a fixed camera installed on the road using the Convolution Neural Network (CNN) technology. First, license plate images and non-license plate images are applied to a previously learned CNN model (AlexNet) to extract and classify features. Then, after detecting the moving vehicle in the video, CNN detects the license plate area by comparing the features of the license plate region with the features of the license plate area. Experimental result shows relatively good performance in various environments such as incomplete lighting, noise due to rain, and low resolution. In addition, to protect personal information this proposed system can also be used independently to detect the license plate area and hide that area to secure the public's personal information.

• Journal of Korea Multimedia Society
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• v.2 no.3
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• pp.297-307
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• 1999

A character recognition system, where a large amount of character images arrive continuously in real time, must preprocess character images very quickly. Moreover, information loss due to image trans-formations such as geometric normalization and thinning needs to be minimized especially when character images are small and noisy. Therefore, we suggest a prompt and effective feature extraction method without transforming original images. For this, boundary pixels are defined in terms of the degree in classification, and those boundary pixels are considered selectively in extracting features. The proposed method is tested by a handwritten character recognition and a car plate number recognition. The experiments show that the proposed method is effective in recognition compared to conventional methods. And an overall reduction of execution time is achieved by completing all the required processing by a single image scan.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1531-1540
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• 2016

In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system.