• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.3
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• pp.111-121
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• 2016

In this study, measurements were conducted by image processing to do an in-depth evaluation of strain of rebar in a uniaxial tension test. The distribution of strain and the necking region were evaluated. The image processing is used to analyze the color information of a colored image, so that the parts consistent with desired targets can be distinguished from the other parts. After this process, the image was converted to a binary one. Centroids of each target region are obtained in the binary images. After repeating such process on the images from starting point to the finishing point of the test, elongation between targets is calculated based on the centroid of each target. The tensile test were conducted on grade 60 #7(D22) and #9(D29) rebars fabricated in accordance with ASTM A615 standards. Strain results from image processing were compared to the results from a conventional strain gauge, in order to see the validity of the image processing. With the image processing, the measuring was possible in not only the initial elastic region but also the necking region of more than 0.5(50%) strain. The image processing can remove the measuring limits as long as the targets can be video recorded. It also can measure strain at various spots because the targets can easily be attached and detached. Thus it is concluded that the image processing helps overcome limits in strain measuring and will be used in various ways.

• Korean Journal of Optics and Photonics
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• v.17 no.3
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• pp.231-239
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• 2006

In recent years, a hierarchical security architecture has been widely studied because it can efficiently protect information by allowing an authorized user access to the level of information. However, the conventional hierarchical decryption methods require several decryption keys for the high level information. In this paper, we propose a hierarchical image encryption using random phase masks and Walsh code having orthogonal characteristics. To decrypt the hierarchical level images by only one decryption key, we combine Walsh code into the hierarchical level system. For encryption process, we first perform a Fourier transform for the multiplication results of the original image and the random phase mask, and then expand the transformed pattern to be the same size and shape of Walsh code. The expanded pattern is finally encrypted by multiplying with the Walsh code image and the binary phase mask. We generate several encryption images as the same encryption process. The reconstruction image is detected on a CCD plane by a despread process and Fourier transform for the multiplication result of encryption image and hierarchical decryption keys which are generated by Walsh code and binary random phase image. Computer simulations demonstrate that the proposed technique can decrypt hierarchical information by using only one level decryption key image and it has a good robustness to the data loss such as random cropping.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.33-42
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• 2020

There has been difficulties in identifying objects by relying on the naked eye in various surveillance systems. There is a growing need for automated surveillance systems to replace soldiers in the field of military surveillance operations. Even though the object detection technology is developing rapidly in the civilian domain, but the research applied to the military is insufficient due to a lack of data and interest. Thus, in this paper, we applied one of deep learning algorithms, Convolutional Neural Network-based binary classification to develop an autonomous identification model of both friend and foe helicopters (AH-64, Mi-17) among the military weapon systems, and evaluated the model performance by considering accuracy, precision, recall and F-measure. As the result, the identification model demonstrates 97.8%, 97.3%, 98.5%, and 97.8 for accuracy, precision, recall and F-measure, respectively. In addition, we analyzed the feature map on convolution layers of the identification model in order to check which area of imagery is highly weighted. In general, rotary shaft of rotating wing, wheels, and air-intake on both of ally and foe helicopters played a major role in the performance of the identification model. This is the first study to attempt to classify images of helicopters among military weapons systems using CNN, and the model proposed in this study shows higher accuracy than the existing classification model for other weapons systems.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.52-63
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• 2021

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3188-3207
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• 2017

In this paper, we propose an image enhancement algorithm to improve license plate extraction rate in various environments (Day Street, Night Street, Underground parking lot, etc.). The proposed algorithm is composed of image enhancement algorithm and license plate extraction algorithm. The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image. The proposed algorithm employs an interpolated probability distribution value (PDV) in order to control a sudden change in image brightness. Probability distribution value can be calculated using cumulative distribution function (CDF) and probability density function (PDF) of the captured image, whose values are achieved by brightness distribution of the captured image. Also, by adjusting the image enhancement factor of each part region based on image pixel information, it provides a function that can adjust the gradation of the image in more details. This processed gray image is converted into a binary image, which fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour by using morphology operations. Then license plate region is detected based on aspect ratio and license plate size of the bound box drawn on connected license plate areas. The images have been captured by using a video camera or a personal image recorder installed in front of the cars. The captured images have included several license plates on multilane roads. Simulation has been executed using OpenCV and MATLAB. The results show that the extraction success rate is more improved than the conventional algorithms.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.116-123
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• 1994

A novel opto-digital pattern recognition method which has shift, rotation, and scale invariant properties is proposed for recognizing two dimensional images having straight lines. The algorithm is composed of three stages. In the first stage the line features of the image are extracted. The second stage imposes the shift, rotation, and scale invariant properties on the extracted features through normalizing procedure. The required normalizing equations are analytically explained. In the last stage, the artificial feedforward neural network is trained with the extracted features. In order to evaluated the proposed algorithm, nine different edge enhnaced binary images composed of straight lines are tested. Thus the proposed algorithm can recognize the patterns event though they are shifted, rotated, and scaled.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.425-435
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• 2014

The research for extracting man-made features such as building and road from the aerial photograph or satellite imagery has been performed actively. As lately the resolution of digital aerial photographs was improved, unwanted features(noise) would be often detected. An edge detection algorithm is developed to make up for such a noise problem, make boundaries of wanted objects clear and extract only needed features. The algorithm developed in this research performs separating RGB channels, differencing between channels, transforming in to binary images, excluding noises and restoring shapes, and edge extraction in order. The images to be used for edge detection are prepared through bundle adjustment, DTM extraction, orthorectification and mosaicking. The roof edges of small building on preprocessed digital aerial orthophotos were extracted using the algorithm developed in this study. The validity of the algorithms was proved by comparing edge results of small building extracted in this study with those of conventional methods.

• Journal of Korea Multimedia Society
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• v.12 no.2
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• pp.191-199
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• 2009

In this paper, the improved edge extract methods is proposed in order to extract edge. For the correct and fast detect, the binary image using the threshold value is applied for a experiment. For the experimental analysis, we compare the existing edge methods with the improved methods. Hereby, the exist methods are the sobel, robert, and prewitt. and the improved methods use the existing methods which is applied mask variations. The merits of the improved mothods have a result of a little erosion, a apparent edge. Specially, we use the grey image of medical image for the experimental analysis and then apply threshold value for a result image. After that, we acquire a apparent edge. For a quantitative analysis of the each methods, the each images was applied a histogram. As a result, we prove the merit of the improved methods using a analytical graph of the medical images.