• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.174-177
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• 2000

자동차 번호판 인식 시스템을 구현하기 위해서는 영상에서 번호판을 추출하는 영역과 추출된 번호판에서 각 문자의 숫자를 추출하는 영역, 마지막으로 이를 인식하는 영역으로 나누어진다. 본 논문에서는 번호판 영역이 다른 영역보다 녹색의 밀집도가 높다는 특징을 이용하여 이미지에서 번호판을 추출하고, 개선된 퍼지 ART학습 알고리즘으로 자동차 번호판 인식에 적용한다. 실험결과에서는 여러 차량에 대해 인식율이 우수한 것을 보인다.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.151-154
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• 2000

차량 번호판 영상을 안정적으로 추출하여 인식하는 방법에는 여러 가지 땅법들이 제시되어 왔다. 기존의 연구들은 번호판 영역 추출에는 높은 성공률을 보이고 있으나 상대적으로 문자 인식의 성공률이 그에 미치지 못해서 전체적인 인식 성공률에 저하를 가져오는 경우가 대부분 이었다. 따라서 본 연구에서는 칼라 정보를 이용하여 입력 영상의 밝기 보정과 번호판 영역을 추출하고 N4M (Normalized 4 - Mash)을 적용하여 문자인식 처리 시간을 단축시키고 인식글을 향상시킬 수 있었다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.342-345
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• 2002

차량 번호판 인식에 대한 여러 가지 방법들이 제시되고 있다. 기존 연구들의 문제점은 번호판 영역의 밝기 변화 둥에 영향을 많이 받았으며 그로 인하여 번호판 영역 추출률에 영향을 미치는 것은 물론 문자 인식에서도 많은 문제점들이 존재하였다. 따라서 본 연구에서는 색상 정보를 이용하여 밝기를 보정한 후 마스크 적용을 통한 수평ㆍ수직 에지 검출과 형태학적 정보를 이용하여 번호판을 추출하고 변형된 특징 매칭을 이용하여 문자를 인식함으로써 인식률을 향상시킬 수 있었다.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.453-462
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• 2021

A lot of license plate data is required for car number recognition. License plate data needs to be balanced from past license plates to the latest license plates. However, it is difficult to obtain data from the actual past license plate to the latest ones. In order to solve this problem, a license plate recognition study through deep learning is being conducted by creating a synthetic license plates. Since the synthetic data have differences from real data, and various data augmentation techniques are used to solve these problems. Existing data augmentation simply used methods such as brightness, rotation, affine transformation, blur, and noise. In this paper, we apply a style transformation method that transforms synthetic data into real-world data styles with data augmentation methods. In addition, real license plate data are noisy when it is captured from a distance and under the dark environment. If we simply recognize characters with input data, chances of misrecognition are high. To improve character recognition, in this paper, we applied the DeblurGANv2 method as a quality improvement method for character recognition, increasing the accuracy of license plate recognition. The method of deep learning for license plate detection and license plate number recognition used YOLO-V5. To determine the performance of the synthetic license plate data, we construct a test set by collecting our own secured license plates. License plate detection without style conversion recorded 0.614 mAP. As a result of applying the style transformation, we confirm that the license plate detection performance was improved by recording 0.679mAP. In addition, the successul detection rate without image enhancement was 0.872, and the detection rate was 0.915 after image enhancement, confirming that the performance improved.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.81-103
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• 2005

The Neuron structure in a nervous system consists of inhibitory neurons and excitory neurons. Both neurons are activated by agonistic neurons and inactivated by antagonist neurons. In this paper, we proposed a physiological fuzzy neural network by analyzing the physiological neuron structure in the nervous system. The proposed structure selectively activates the neurons which go through a state of excitement caused by agonistic neurons and also transmit the signal of these neurons to the output layers. The proposed physiological fuzzy neural networks based on the nervous system consists of a input player, and the hidden layer which classifies features of learning data, and output layer. The proposed fuzzy neural network is applied to recognize bronchial squamous cell carcinoma images and car plate images. The result of the experiments shows that the learning time, the convergence, and the recognition rate of the proposed physiological fuzzy neural networks outperform the conventional neural networks.

• 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 Journal of the Korea institute of electronic communication sciences
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• v.12 no.4
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• pp.651-656
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• 2017

In this study, we designed the system that helps drivers check all information about parking space at the entrance and find out whether the places is available or not, because the system has 'Image recognition function' which can even recognize car number plates exactly. Besides, we place the webcam close to the car number plate, so that car number can be identified more quickly. Finally, since we set the webcam high, the system keeps us from parking wrong places by displaying on the screen.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1093-1098
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• 2017

In this study, we designed the system that helps drivers check all information about parking space at the entrance and find out whether the places is available or not, because the system has 'Image recognition function' which can even recognize car number plates exactly. Besides, we place the webcam close to the car number plate, so that car number can be identified more quickly. Finally, since we set the webcam high, the system keeps us from parking wrong places by displaying on the screen.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.407-411
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• 2007

이 논문은 우리나라 차량 영상에서 번호판 영역을 추출하는 알고리즘을 제안한다. 우리나라 번호판은 하단에 네개의 숫자를 포함하고 있으므로, 네 개의 숫자를 찾으면 번호판을 추출 할 수 있다. 제안하는 방법은 입력된 영상에서 숫자의 가능성을 가진 연결 요소를 검출하고 이들을 군집화 한다. 군집화 된 연결요소들을 바탕으로 숫자 네개(4-digits) 후보를 생성한다. 4-digits 후보들을 인식하여 숫자의 가능성을 측정하고, 적합도로 변환한다. 후보영역 중 적합도가 가장 높은 영역을 번호판 영역으로 추출한다. 적합도는 Perfect Metrics 방법으로 측정하였다. 제안하는 방법을 주간 영상 4600장과 야간 영상 264장으로 테스트 한 결과 각각 97.23%, 95.45%의 검출률과 0.09%, 0.11%의 오검출률을 얻었다.