• 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.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.965-967
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• 1995

One of the most difficult tasks in the process of automatic vehicle license plate recognition is the extraction of each character from within license plate region. In many cases, characters, especially serial numbers of plates are connected together due to noise and plate accessories. The recognition process may not be successful without extracting these characters effectively. This paper presents an algorithm to extract these connected characters very effectively. The algorithm utilizes mathematical morphology, connected component analysis, and gradient filters for character extraction. The paper also presents thorough experimental results as well as details of the algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.6
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• pp.535-543
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• 2009

This paper describes a design for RFID(Radio Frequency Identification) tag antenna using license plate attached the vehicle bumper in 900 MHz band. The proposed tag antenna size which is located on upper center position of a vehicle license plate is 162.5${\times}$40${\times}$1 mm$^3$. A resonant frequency of design antenna and the bandwidth which has return loss of -10 dB below are 900 MHz and about 720 MHz(640${\sim}$1,360 MHz), respectively. The commercial chip impedance considered on design was 16- j131 ${\Omega}$ and the complex conjugate impedance of chip was used as input impedance of tag antenna. The measured return loss and radiation pattern were agreed well with the calculated results. The measured readable range of the proposed tag antenna designed on only the vehicle license plate was 11.5 m. Moreover, its range of the fabricated tag antenna that the license plate and the vehicle bumper were fixed by volt and nut was observed 10.4 m. These measured readable range showed about 5 m above far distance more than the average readable range of commercial tag antenna.

• 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 KIEE Conference
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• 2007.07a
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• pp.1583-1584
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• 2007

To test the performance of the proposed algorithm, images of seventy vehicle were tested. The success rates for license plate and character recognition were approximately 98.4% and 96.3%, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.78-84
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• 2016

This paper presents and implements a vehicle speed measurement system using a license plate recognition system and a stereo camera. Using the feature points of the license plate recognition system, the disparity information is extracted and then the distance to the feature points is calculated by using the disparity information. In this paper, a vehicle speed is measured using the adjacent distances from consecutive stereo images and the corresponding time of the distances. Actual vehicle speed is also measured using the reference measurement equipment (tape switch based system) in order to test the accuracy of the proposed speed measurement system. The implemented stereo based speed measurement system shows appropriate result within specification both in the daytime and nighttime experiments.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.1002-1005
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• 1995

This paper presents an enhanced preprocessing and recognition algorithm for automatic vehicle license plate recognition system. The algorithm first applies horizontal gradient filter followed by thresholding and mathematical morphology operation for preprocessing. The final stage of the preprocessing is the application of connected component analysis in order to estimate the license plate region. For the recognition of the serial numbers of the plates, we developed a very effective algorithm. We call this zerocrossing count algorithm. This paper presents a detail of this algorithm and compare the performance with a template matching algorithm which utilizes correlation coefficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.909-919
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• 2014

License plate (LP) detection is the most imperative part of an automatic LP recognition (LPR) system. Typical LPR contains two steps, namely LP detection (LPD) and character recognition. In this paper, we propose an efficient Vehicle-to-LP detection framework which combines with an adaptive GMM (Gaussian Mixture Model) and a cascade of boosted classifiers to make a faster vehicle LP detector. To develop a background model by using a GMM is possible in the circumstance of a fixed camera and extracts the motions using background subtraction. Firstly, an adaptive GMM is used to find the region of interest (ROI) on which motion detectors are running to detect the vehicle area as blobs ROIs. Secondly, a cascade of boosted classifiers is executed on the blobs ROIs to detect a LP. The experimental results on our test video with the resolution of $720{\times}576$ show that the LPD rate of the proposed system is 99.14% and the average computational time is approximately 42ms.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.429-431
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• 2012

Vehicle-logo recognition technology is very important in vehicle automatic recognition technique. The intended application is automatic recognition of vehicle type for secure access and traffic monitoring applications, a problem not hitherto considered at such a level of accuracy. Vehicle-logo recognition can improve Vehicle type recognition accuracy. So in this paper, introduces how to vehicle-logo recognition. First introduces the region of the license plate by algorithm and roughly located the region of car emblem based on the relationship of license plate and car emblem. Then located the car emblem with precision by the distance of Hausdorff. On the base, processing the region by morphologic, edge detection, analysis of connectivity and pick up the PCA character by lowing the dimension of the image and unifying the PCA character. At last the logo can be recognized using the algorithm of support vector machine. Experimental results show the effectiveness of the proposed method.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1649-1657
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• 2008

In this paper, to overcome the failure of binarization for the characters suffered from low contrast and non-uniform illumination in license plate character recognition system, we improved the binarization method by combining local thresholding with global thresholding and edge detection. Firstly, apply the local thresholding method to locate the characters in the license plate image and then get the threshold value for the character based on edge detector. This method solves the problem of local low contrast and non-uniform illumination. Finally, back-propagation Neural Network is selected as a powerful tool to perform the recognition process. The results of the experiments i1lustrate that the proposed binarization method works well and the selected classifier saves the processing time. Besides, the character recognition system performed better recognition accuracy 95.7%, and the recognition speed is controlled within 0.3 seconds.