• Imaging Science in Dentistry
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• v.49 no.3
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• pp.219-228
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• 2019

Purpose: The aim of this study was to determine the reasons and solutions for intraoral phosphor storage plate (PSP) image artifacts and errors, and to develop an appropriate classification of the artifacts. Materials and Methods: This study involved the retrospective examination of 5,000 intraoral images that had been obtained using a phosphor plate system. Image artifacts were examined on the radiographs and classified according to possible causative factors. Results: Artifacts were observed in 1,822 of the 5,000 images. After examination of the images, the errors were divided into 6 groups based on their causes, as follows: images with operator errors, superposition of undesirable structures, ambient light errors, plate artifacts (physical deformations and contamination), scanner artifacts, and software artifacts. The groups were then re-examined and divided into 45 subheadings. Conclusion: Identification of image artifacts can help to improve the quality of the radiographic image and control the radiation dose. Knowledge of the basic physics and technology of PSP systems could aid to reduce the need for repeated radiography.

• Journal of IKEEE
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• v.21 no.4
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• pp.412-415
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• 2017

In this paper, we propose a multi license plate recognition system using high resolution $360^{\circ}$ omnidirectional IP camera. The proposed system consists of a planar division part of $360^{\circ}$ circular image and a multi license plate recognition part. The planar division part of the $360^{\circ}$ circular image are divided into a planar image with enhanced image quality through processes such as circular image acquisition, circular image segmentation, conversion to plane image, pixel correction using color interpolation, color correction and edge correction in a high resolution $360^{\circ}$ omnidirectional IP Camera. Multi license plate recognition part is through the multi-plate extraction candidate region, a multi-plate candidate area normalized and restore, multiple license plate number, character recognition using a neural network in the process of recognizing a multi-planar imaging plates. In order to evaluate the multi license plate recognition system using the proposed high resolution $360^{\circ}$ omnidirectional IP camera, we experimented with a specialist in the operation of intelligent parking control system, and 97.8% of high plate recognition rate was confirmed.

• The Journal of Korean Society for Radiation Therapy
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• v.17 no.1
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• pp.45-55
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• 2005

Purpose : To evaluate the usefulness of the handmade patient immobilization device and to report the clinical results of it. Materials and methods : We made two fusion images and analyzed those images. One image is made with diagnostic MR image and CT image, the other with therapeutic planning MR image and CT image. With open head holder, we measured the skin dose and attenuation dose. Also, we made the planning CT couch plate with acrylic plate and styrofoam and compared artifact. Results : We could get more accurate fusion image when we use MR head holder(within 2mm error). The skin dose was reduced 2 times and the attenuation dose was reduced more than $20\%$ when open head holder used. The planning CT couch plate was more convenient than conventional board and reduced artifact remarkably. Conclusion : We could verify the localization point in the MR image which is taken with MR head holder. So we could fuse the image more accurately. The same method could be applied to PET and US image, if the alike immobilization device used. With open head holder, the skin dose and the attenuation dose was reduced. And those above devices could substitute for expensive foreign device, if those are manufactured adequately.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.1-8
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• 2010

Recently, the character image processing technology using portable device camera image at home and abroad are actively conducted, but Practical use are lower rate because of accuracy and time-consuming process problems. In this paper, we propose the license plate recognition method based on geometric information from portable device camera image. In the extracted license plate region we recognize characters using the chain code and the Thickness information through the cumulative projected edge after performing the pre-processing work considering the angle difference, the contrast enhancement and the low resolution from portable device camera image. The proposed algorithm is effective and accurate recognition by light and reducing the processing time. And, the results from the character recognition success rate was 95%. In the future, we will research about license plate recognition algorithm using long distance image or added motion blur image.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.173-179
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• 2012

In this paper, we presents a new method for vehicle number plate detection. Our method is basically the method extracting a rectangles from a car image because the shape of a vehicle number plate is a rectangle. For detecting the vehicle number plate, firstly, the contrast of the input image is enhanced. Then, the lines in the image are obtained by using LSD(line segment detector), and rectangles in the image are detected from the line data. These rectangles are the candidates of the car plate, from which the car plate is selected. In this procedure, the method of detecting rectangles is our proposed method, which consists of three stages: (1) extracting corners from the line segments by LSD; (2) extracting diagonal lines from the corner data; and (3) detecting rectangles from diagonal line information. And finally the vehicle number plate is selected from these rectangles by using the feature of the vehicle number plate and the inside information of rectangles. In the experiments with the 100 images captured by our digital camera, we have achieved a detection rate of 94%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.1987-1996
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• 2009

Recently, the color of vehicle license plate has been changed from green to white. Thus the vehicle plate recognition system used for parking management systems, speed and signal violation detection systems should be robust to the both colors. This paper presents a vehicle license plate recognition system, which works on both of green and white plate at the same time. In the proposed system, the image of license plate is taken from a captured vehicle image by using morphological information. In the next, each character region in the license plate image is extracted based on the vertical and horizontal projection of plate image and the relative position of individual characters. Finally, for the recognition process of extracted characters, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis) are sequentially utilized. In the experiment, vehicle license plates of both green background and white background captured under irregular illumination conditions have been tested, and the relatively high extraction and recognition rates are observed.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.87-94
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• 2005

This paper describes the method of detecting the region of vehicle number plate in colored car image with number plate. Vehicle number plate region generally shows formula colors in accordance with type of car. According to this, we use the method to combine a color ingredient H of HSI color model and a color ingredient Q of YIQ color model. However, the defect which a total operation time takes much exists if it uses such method. Therefore, in this paper, the concurrent accomplishes a candidate area extraction operation as draw a color H and Q ingredient among steps of extracting a region of vehicle number Plate. After the above step, as a next step in combination with color H and Q we can accomplish an region extraction fast by comparing to candidate regions extracted from each steps not to do a comparison operation to all of image pixel information. We also show implementation results Processed at each steps and compare with extraction time according to image resolutions.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.29 no.2
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• pp.397-405
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• 1999

Purpose: To clarify the usefulness and the limitation of Digora system/sup (R)/ by evaluating the physical characteristics as the quantitative image on Image Plate(Ip). Materials and Methods: Radiograms were taken by Heliodent MD(Siemens Co.. Germany) with the image plate for adult. Cu-step wedge as reference material. and three pieces of dry mandibular bone. Image analysis was performed by single color enhancement. density measurement with histogram. The relationship between the exposure conditions and the distribution of the pixel values of the image. the variation of pixel values of each step of Cu-step wedge at two different area and Cu-equivalent value of three pieces of dry mandibular bone measure by the conversion equation. Results: There was no linear relationship between the exposure condition and the average pixel value of the image. of which the distribution was not even. The pixel value differences between the center portion and the periphery were ranged from 60 to 70 in vertical plane and from 15 to 26 in horizontal plane. Two plot profile formed at two different areas of the Cu-step wedge were different. The measured Cu-equivalent values showed the discrepancy among the times of measurement. Conclusion: As above results. Image Plate(Ip) of Digora system/sup (R)/ showed the limitation as the quantitative image. The physical property of IP was expected to need to be compensated for the quantitative evaluation of the bone or others

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.121-126
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• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.