• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.4
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• pp.624-631
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• 2002

In this paper, an effective digital watermarking method for copyright protection of binary image data is proposed. First a binary image is grouped into feature regions which has geometrical features and general one. The watermark for authentication is embedded in general regions in order to preserve geometrical features regions. We have used run-length code and special runs for grouping feature regions and general one. For invisibility of watermark, we have embedded the watermark considering transition sensitivity of each pixel in general regions. The proposed method is applied some binary image such as character, signature, seal, and fingerprint image to evaluate performance. By the experimental results, the proposed method preserve feature regions of original image and have higher invisibility of watermarks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.379-381
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• 2013

In this paper, we propose a method to detect the gradient of the spaces between sentences and to deskew in the document image. First, gradient is measured by pixels for spaces between sentences that has been done an edge extraction in document image and then skewed image is corrected by using the value of the gradient which has been measured. Since document image is divided into several areas, it shows a robust processing result by handling the margin, images, and multistage form in the document. Because the proposed method does not use pixel of the character region but use the blank area, degraded document image as well as vivid document image is effectively corrected than conventional method.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.497-502
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• 2016

Shape Contexts Recognition(SCR) is a technology recognizing shapes such as figures and objects, greatly supporting technologies such as character recognition, motion recognition, facial recognition, and situational recognition. However, generally SCR makes histograms for all contours and maps the extracted contours one to one to compare Shape A and B, which leads to slow progress speed. Thus, this paper has made simple yet more effective algorithm with optimized contour, finding the outlines according to shape figures and using the improved Douglas-Peucker algorithm and Harris corner detector. With this improved method, progress speed is recognized as faster.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.842-850
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• 1998

In this paper, we propose a method that recognize hatched area based on segmentation and vectorization of a machine drawing. This recogntion of hatched area is composed of three parts. First, the proposed method segments an object, arrowheads and interpretation lines from the machine drawing and vectorizes the object and interpretation lines. Second, closed-loops are labeled with the vectorized objects, and then candidates of hatched areas arc determined. Finally, by recognizing hatched lines included in hatched areas, recognition of the hatched areas is completed. The proposed method is more useful in extracting and recognizing the hatched areas.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.733-735
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• 2018

최근의 차량 주차관리 시설, 출입통제가 필요한 장소 그리고 도로 방범카메라를 통한 단속 등 다양한 곳에서 차량 번호판 자동 인식 기술들이 활용되고 있다. 하지만 현재 사용되고 있는 LPR(License Plate Recognition) 시스템에는 많은 장비와 비용이 들어간다는 큰 단점이 존재한다. 본 논문에서는 하나의 컴퓨터와 최소의 카메라를 가지고 할 수 있는 기계학습을 통한 영상처리를 제안하려 한다. 먼저 딥러닝 프레임워크 중 하나인 YOLO(You Only Look Once) [4]를 활용하여 자동차의 번호판 부분의 영역을 검출하고 Grayscale를 통해 햇빛 또는 조명 등의 영향을 감소시켜 번호판의 특징을 보존시킨다. 전처리 작업이 끝난 후 번호판에서 숫자를 인식 하는 부분에서는 k-NN(k-Nearest Neighbor) 알고리즘을 사용하였으며 한글 문자 인식부분은 Template Matching을 이용하였다. 제안한 알고리즘을 사용하여 기존 LPR 시스템에서 획득한 차량이미지를 대상으로 시뮬레이션 한 결과 좋은 결과를 얻을 수 있어 향후 연구 방향의 시스템 확장성의 가능성을 발견할 수 있었다.

• Journal of Information Technology Services
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• v.20 no.6
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• pp.97-105
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• 2021

Recently, with the development of IT technology, unmanned systems are being introduced in many industrial fields, and one of the most important factors for introducing unmanned systems in the automobile field is vehicle licence plate recognition(VLPR). The existing VLPR algorithms are configured to use image processing for a specific type of license plate to divide individual areas of a character within the plate to recognize each character. However, as the number of Korean vehicle license plates increases, the law is amended, there are old-fashioned license plates, new license plates, and different types of plates are used for each type of vehicle. Therefore, it is necessary to update the VLPR system every time, which incurs costs. In this paper, we use an object detection algorithm to detect character regardless of the format of the vehicle license plate, and apply a handwritten Hangul recognition(HHR) algorithm to enhance the recognition accuracy of a single Hangul character, which is called a Hangul unit. Since Hangul unit is recognized by combining initial consonant, medial vowel and final consonant, so it is possible to use other Hangul units in addition to the 40 Hangul units used for the Korean vehicle license plate.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.1-11
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• 2022

Recently, research on license plate recognition, which is a core technology of an intelligent transportation system(ITS), is being actively conducted. In this paper, we propose a method to extract numbers and characters from low-quality license plate images by applying the YOLOv4 algorithm. YOLOv4 is a one-stage object detection method using convolution neural network including BACKBONE, NECK, and HEAD parts. It is a method of detecting objects in real time rather than the previous two-stage object detection method such as the faster R-CNN. In this paper, we studied a method to directly extract number and character regions from low-quality license plate images without additional edge detection and image segmentation processes. In order to evaluate the performance of the proposed method we experimented with 500 license plate images. In this experiment, 350 images were used for training and the remaining 150 images were used for the testing process. Computer simulations show that the mean average precision of detecting number and character regions on vehicle license plates was about 93.8%.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.915-924
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• 2002

Conventional Systems for parcel sorting consist of two sequences as loading the parcel into conveyor belt system and post-code input. Using bar code information, the parcels to be recorded and managed are recognized. This paper describes a 32 $\times$ 32 sized mini-block inspection to extract bar code Region of Interest (ROI) from the line Charged Coupled Device (CCD) camera capturing image of moving parcel at 2m/sec speed. Firstly, the Min-Max distribution of the mini-block has been applied to discard the background of parcel and region of conveying belts from the image. Secondly, the diagonal inspection has been used for the extraction of letters and bar code region. Five horizontal line scanning detects the number of edges and sizes and ROI has been acquired from the detection. The wrong detected area has been deleted by the comparison of group size from labeling processes. To correct excluded bar code region in mini-block processes and for analysis of bar code information, the extracted ROI 8 boundary points and decline distribution have been used with central axis line adjustment. The ROI extraction and central axis creation have become enable within 60～80msec, and the accuracy has been accomplished over 99.44 percentage.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.12 no.2
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• pp.97-105
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• 2014

In the case of the direct measurement of $^{226}Ra$ using a HPGe gamma-ray spectrometer, the interference between gammarays with 186.21 keV of $^{226}Ra$ and 185.7 keV of $^{235}U$ should be corrected to calculate the net peak area in the energy spectrum. In general, it is very difficult to conduct peaks stripping with difference of about 0.5 keV, although a HPGe with the superior resolution is applied and the maximum channels is applied to the spectrometer. In this study, several interference correction techniques in the direct measurement were surveyed to evaluate the feasibility for the measurement of $^{226}Ra$ using the gamma-ray spectrometery. Applying the interference corrections to the analysis of raw materials and by-products, the method validation for the direct measurement of $^{226}Ra$ was conducted by evaluating the measurement uncertainty, linearity, and range. As a result, the optimum method of the interference correction was selected by comparing with the indirect measurement of which progenies of $^{226}Ra$, such as $^{214}Pb$ and $^{214}Bi$, were analyzed in the secular equilibrium state.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.253-260
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• 2009

Many algorithms for computer vision and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation has two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job that needs much cooperation between CPU and GPU, which is usual in image processing and pattern recognition contrary to the graphic area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results in effectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text extraction system using the proposed architecture, and the computational times showed about 15 times faster than implementation on only GPU without OpenMP.