• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1197-1199
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• 2009

Self-alignment for the fabrication of printed thin film transistors has become of great interest because of the resolution and registration limits of printing technologies. In this work, self-patterning and selfregistration processes are introduced, which do not need surface energy patterning and the resulting minimum gate channel length could be down to $11.2{\mu}m$ with the sheet resistance of 2.6 ${\Omega}/{\square]$ for the source and drain electrodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.180-194
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• 2021

For small image registration, feature-based approaches are likely to fail as feature detectors cannot detect enough feature points from low-resolution images. The classic FFT approach's prediction accuracy is high, but the registration time can be relatively long, about several seconds to register one image pair. To achieve real-time and high-precision rigid registration for small images, we apply deep neural networks for supervised rigid transformation prediction, which directly predicts the transformation parameters. We train deep registration models with rigidly transformed CIFAR-10 images and STL-10 images, and evaluate the generalization ability of deep registration models with transformed CIFAR-10 images, STL-10 images, and randomly generated images. Experimental results show that the deep registration models we propose can achieve comparable accuracy to the classic FFT approach for small CIFAR-10 images (32×32) and our LSTM registration model takes less than 1ms to register one pair of images. For moderate size STL-10 images (96×96), FFT significantly outperforms deep registration models in terms of accuracy but is also considerably slower. Our results suggest that deep registration models have competitive advantages over conventional approaches, at least for small images.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.117-128
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• 2012

Recently IT infrastructure plays a central role in the base of the society. However, use of personal registration number on internet sites has become a major factor increasing danger of leaking of personal information. Currently, the government is recommending various information protection services in order to prevent the collection of personal registration numbers and leaking of personal information on the internet. Among them, i-PIN service is the one recommended for minimal use of personal registration numbers on the internet. Although i-PIN can be used as a way to substitute personal registration numbers on the internet, there are certain limitations in using i-PIN as the only key to recognize individuals. This study proposes organization of web system in which self certification can be conducted using i-PIN as a tool for personal certification. Also its usability and stability have been verified through performance test when i-PIN service is linked with web service and used as personal certificate. But i-PIN service is unavailable if obstacles occur in providers of i-PIN self certification. To settle this inconvenience, the study also proposes how to cope with such obstacles.

• Journal of Software Engineering Society
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• v.25 no.1
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• pp.19-28
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• 2012

Personal registration number has been used as a means of personal identification on existing internet. However, its use on internet sites has become a major factor increasing danger of leaking of personal information. Presently, the government recommends i-PIN to minimize the collection of personal registration numbers and leaking of personal information on internet. Original purpose of i-PIN is to recognize persons by its virtual number of 13 digits instead of using personal registration number on internet websites. These days, i-PIN continues to be used increasingly as a form of certification. This study seeks to explore software service methodology of using i-PIN as a form of certification on internet websites and examples in which its other forms of self certification are used than existing i-PIN services.

• Journal of muscle and joint health
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• v.27 no.2
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• pp.153-159
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• 2020

Purpose: This study aims to identify the effect of university students' intention to donate hematopoietic stem cells based on the theory of planned behavior. Methods: The subjects include university students who visited the campaign for the registration of hematopoietic stem cell donation held at H university on September 28, 2019 and October 2, 2019. Results: The intention to register for hematopoietic stem cell donation and empirical attitude (r=.72, p<.001), instrumental attitude (r=.64, p<.001), directive norm (r=.53, p<.001), technical norms (r=.55, p<.001) and self-efficacy (r=.86, p<.001) showed a significant positive correlation. The multiple regression analysis revealed that self-efficacy (β=.66) and empirical attitude (β=.23) were the most influential factors. Conclusion: Educational and promotional programs to increase the intention to register for hematopoietic stem cell donation need to be developed to help increase students' self-efficacy and help them develop a positive experiential attitude. In addition, further research is needed to determine whether the intention to register in hematopoietic stem cell donation among university students can lead to actual registration and donation after registration.

• Journal of Agricultural Extension & Community Development
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• v.28 no.4
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• pp.215-229
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• 2021

This study aims to identify the factors influencing the registration of managers or co-managers in agricultural management in the field of work and life of female farmers and to derive implications for expanding the registration of female farmers as managers and securing the status of women. In this study, binomial logit analysis was conducted with 1,043 questionnaire responses collected on&offline to achieve the research purpose. As a result of the analysis, it was found that the duration of farming, decision-making initiative, the experience of the supporting policies for farming activity, the activities of the crop group/research or female farmer's group, and the experience of supporting policies for self-development and leisure activities had a statisticallty significant effect on the registration of female farmers.

• Korean Journal of Geomatics
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• v.5 no.1
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• pp.7-19
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• 2005

Photogrammetric mapping procedures have gone through major developments due to significant improvements in its underlying technologies. The availability of GPS/INS systems greatly assist in direct geo-referencing of the acquired imagery. Still, photogrammetric datasets taken without the aid of positioning and navigation systems need control information for the purpose of surface reconstruction. Point features were, and still are, the primary source of control for the photogrammetric triangulation although other higher-order features are available and can be used. LIDAR systems supply dense geometric surface information in the form of three dimensional coordinates with respect to certain reference system. Considering the accuracy improvement of LIDAR systems in the recent years, LIDAR data is considered a viable supply of photogrammetric control. To exploit LIDAR data, new challenges are poised concerning the representation and reference system by which both the photogrammetric and LIDAR datasets are described. In this paper, registration methodologies will be devised for the purpose of integrating the LIDAR data into the photogrammetric triangulation. Such registration methodologies have to deal with three issues: registration primitives, transformation parameters, and similarity measures. Two methodologies will be introduced that utilize straight-line and areal features derived from both datasets as the registration primitives. The first methodology directly incorporates the LIDAR lines as control information in the photogrammetric triangulation, while in the second methodology, LIDAR patches are used to produce and align the photogrammetric model. Also, camera self-calibration experiments were conducted on simulated and real data to test the feasibility of using LIDAR patches for this purpose.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.253-264
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• 1999

Most autonomous mobile robots view only things in front of them, and as a result, they may collide with objects moving from the side or behind. To overcome this problem. an Active Omni-directional Range Sensor System has been built that can obtain an omni-directional range data through the use of a laser conic plane and a conic mirror. Also, mobile robot has to know its current location and heading angle by itself as accurately as possible to successfully navigate in real environments. To achieve this capability, we propose a self-localization algorithm of a mobile robot using an active omni-directional range sensor in an unknown environment. The proposed algorithm estimates the current position and head angle of a mobile robot by a registration of the range data obtained at two positions, current and previous. To show the effectiveness of the proposed algorithm, a series of simulations was conducted and the results show that the proposed algorithm is very efficient, and can be utilized for self-localization of a mobile robot in an unknown environment.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.739-747
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• 2018

Integration analysis of multi-sensor satellite images is becoming increasingly important. The first step in integration analysis is image registration between multi-sensor. SIFT (Scale Invariant Feature Transform) is a representative image registration method. However, optical image and SAR (Synthetic Aperture Radar) images are different from sensor attitude and radiation characteristics during acquisition, making it difficult to apply the conventional method, such as SIFT, because the radiometric characteristics between images are nonlinear. To overcome this limitation, we proposed a modified method that combines the SAR-SIFT method and shape descriptor vector DLSS(Dense Local Self-Similarity). We conducted an experiment using two pairs of Cosmo-SkyMed and KOMPSAT-2 images collected over Daejeon, Korea, an area with a high density of buildings. The proposed method extracted the correct matching points when compared to conventional methods, such as SIFT and SAR-SIFT. The method also gave quantitatively reasonable results for RMSE of 1.66m and 2.45m over the two pairs of images.