• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.633-636
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• 2015

KaVA (KVN and VERA Array) is a new combined VLBI array composed of KVN (Korean VLBI Network) and VERA (VLBI Exploration of Radio Astrometry). Here, we report the following two issues. (1) We review the initial results of imaging observations of M87 at 23 GHz following Niinuma et al. (2014). The KaVA images reveal extended outflows including complex substructures such as knots and limb-brightening, in agreement with previous VLBI observations. KaVA achieves a high dynamic range of ~1000, more than three times better than that achieved by VERA alone. (2) Based on subsequent observations and discussions led by the KaVA AGN SubWorking Group, we set monitoring observations of Sgr $A^{\ast}$ and M87 as our Key Science Project (hereafter KSP) because of the closeness and largeness of their central super-massive black holes. The main science goals of the KSP are (i) testing the magnetically-driven-jet paradigm by mapping velocity fields of the M87 jet, and (ii) obtaining tight constraints on physical properties of the radio emitting region in Sgr $A^{\ast}$. Towards KSP, we show the first preliminary images of M87 at 23 GHz and Sgr $A^{\ast}$ at 43 GHz with the bandwidth of 256 MHz.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.61-65
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• 2003

In this paper, a novel watermarking technique that based on wavelet packet transform using PIM is proposed for the copyright of the digital images. We divide wavelet packet coefficients into detailed blocks and calculate the complexity of each block using PIM. Because human is impervious to the change in the complex area, the embedding watermark to the selected coefficients using PIM enhances invisibility. From the experimental results, the proposed algorithm shows better invisibility and robustness performance in a general signal processing such as JPEG and SPIHT lossy compression, median filtering. Especially, it demonstrates better performance for the low bit-rate compression in the images that include many high frequency components.

• Korean Journal of Geomatics
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• v.4 no.2
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• pp.39-45
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• 2004

Building reconstruction attempts to generate geometric and radiometric models of existing buildings usually from sensory data, which have been traditionally aerial or satellite images, more recently airborne LIDAR data, or the combination of these data. Extensive studies on building reconstruction from these data have developed some competitive algorithms with reasonable performance and some degree of automation. Nevertheless, the level of details and completeness of the reconstructed building models often cannot reach the high standards that is now or will be required by various applications in future. Hence, the use of terrestrial sensory data that can provide higher resolution and more complete coverage has been intensively emphasized. We developed a fusion framework for building reconstruction from terrestrial sensory data, that is, points from a laser scanner, images from digital camera, and absolute coordinates from a total station. The proposed approach was then applied to reconstructing a building model from real data sets acquired from a large complex existing building. Based on the experimental results, we assured that the proposed approach cam achieve high resolution and accuracy in building reconstruction. The proposed approach can effectively contribute in developing an operational system producing large urban models for 3D GIS with reasonable resources.

• Annual Conference of KIPS
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• 2019.05a
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• pp.515-518
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• 2019

Semantic segmentation has remained as a challenging problem in the field of computer vision. Given the immense power of Convolution Neural Network (CNN) models, many complex problems have been solved in computer vision. Semantic segmentation is the challenge of classifying several pixels of an image into one category. With the help of convolution neural networks, we have witnessed prolific results over the time. We propose a convolutional neural network model which uses Fully CNN with deconvolutional pixel layers. The goal is to create a hierarchy of features while the fully convolutional model does the primary learning and later deconvolutional model visually segments the target image. The proposed approach creates a direct link among the several adjacent pixels in the resulting feature maps. It also preserves the spatial features such as corners and edges in images and hence adding more accuracy to the resulting outputs. We test our algorithm on Karlsruhe Institute of Technology and Toyota Technologies Institute (KITTI) street view data set. Our method achieves an mIoU accuracy of 92.04 %.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4005-4025
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• 2019

Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis. CMF is performed by copying and pasting a part of an image into other portions of it. Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult. A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks. Secondly, invariant features for each block are extracted using QPCET. Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching. Lastly, a new technique is introduced to reduce the flat region-mediated false matches. Experiments are performed on numerous images selected from the CoMoFoD database. MATLAB 2017b is used to employ the proposed method. Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method. Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression. Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate. The proposed approach may serve as a basis for accurate digital image forensic investigations.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.

• Journal of Korea Game Society
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• v.21 no.1
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• pp.125-138
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• 2021

This paper analyzed the body images of the posthuman characters in digital games and the nomadic subjects formed through gameplay. Nomadic Subject is the subject with a complex and non-single identity that appears as a posthuman identity. The player experiences posthuman subject directly in the process of controlling characters. Body images of posthuman characters are categorized into three types that imitate idealized bodies, deform the bodies through articulation, and extend the bodies through equipment. The player builds ethical identity by making choices under the constraints of the game.

• Fashion & Textile Research Journal
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• v.23 no.2
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• pp.151-158
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• 2021

The purpose of this study was to develop high-quality naturally dyed leisurewear with images of traditional Korean clothing that keeps a psychologically comfortable and physically pleasant environment at home and in vacation spots. The root bark of elm trees, the atopic skin, is also known to be effective for the relief of rhinitis and atopic diseases as well as stress and insomnia. However, there is insufficient color in the bark for the dyeing of fashion products, so to compensate for the lack of color, for dyeing purposes it was combined with a composite extract called Rubia akane Nakai resulting in a relatively bright red color. Except for the light fastness, all the fastnesses were rated 4 to 5, showing excellent results. Through complex dyeing using elm bark and pods extract the author produced four high-quality vests, one-piece, a gown, and jeogori-pantsuits of silk materials with Korean images that are suitable wear for relaxing comfortably at home and during breaks and which provide a comfortable and physically pleasant experience. The vest was made with the formal style of Bae-ja and Dang-eu, the dress is made of Cheok-lik, and the gown is made of Wonsam. It will be meaningful at a time when the importance of rest is increasing due to the healing clothes worn by busy modern people.

• Advances in nano research
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• v.12 no.2
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• pp.223-239
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• 2022

Object detection has always been to pursue objects with particular properties or representations and to predict details on objects including the positions, sizes and angle of rotation in the current picture. This was a very important subject of computer vision science. While vision-based object tracking strategies for the analysis of competitive videos have been developed, it is still difficult to accurately identify and position a speedy small ball. In this study, deep learning (DP) network was developed to face these obstacles in the study of tennis motion tracking from a complex perspective to understand the performance of athletes. This research has used CNN-VGG 16 to tracking the tennis ball from broadcasting videos while their images are distorted, thin and often invisible not only to identify the image of the ball from a single frame, but also to learn patterns from consecutive frames, then VGG 16 takes images with 640 to 360 sizes to locate the ball and obtain high accuracy in public videos. VGG 16 tests 99.6%, 96.63%, and 99.5%, respectively, of accuracy. In order to avoid overfitting, 9 additional videos and a subset of the previous dataset are partly labelled for the 10-fold cross-validation. The results show that CNN-VGG 16 outperforms the standard approach by a wide margin and provides excellent ball tracking performance.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.653-662
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• 2022

Recently, various researches have been made on a fingerprint classification method using Convolutional Neural Networks (CNN), which is widely used for multidimensional and complex pattern recognition such as images. The CNN-based fingerprint classification method can be executed by integrating the two-step process, which is generally divided into feature extraction and classification steps. Therefore, since the CNN-based methods can automatically extract features of fingerprint images, they have an advantage of shortening the process. In addition, since they can learn various features of incomplete or low-quality fingerprints, they have flexibility for feature extraction in exceptional situations. In this paper, we intend to identify the research trends of CNN-based fingerprint classification and discuss future direction of research through the analysis of experimental methods and results.