• Journal of Digital Convergence
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• v.19 no.10
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• pp.411-418
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• 2021

In this study, information search methods and user interface types provided to users were investigated for the top 100 e-commerce services selected by Statista and the National Retail Federation. And the characteristics of each type were derived by analyzing the interaction method of the user's manipulation with the visualization elements constituting the interface. The research results are as follows. First, as the information provision method, spread format was more often used as the number and hierarchy of information increased, and drop-down and mega menu methods were used more often as the number and hierarchy of information decreased. Second, as a visual classification method according to the information hierarchy, the background color, font change, and line were often used, and there were many cases where the background color and line were used at the same time. Third, there were various elements such as background color, text color, and line as an interaction method for user manipulation, and two or more of them were applied at the same time the most. This study is meaningful in that it defines the characteristics of each type through the analysis of the types of interfaces for e-commerce information search and items that can be the selection criteria for detailed elements.

• Journal of the Korea Computer Graphics Society
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• v.25 no.5
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• pp.1-9
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• 2019

In this paper, we propose a video enhancement method using generative adversarial networks to remove raindrops and restore the background information on the removed region in the coastal wave video imagery distorted by raindrops during rainfall. Two experimental models are implemented: Pix2Pix network widely used for image-to-image translation and Attentive GAN, which is currently performing well for raindrop removal on a single images. The models are trained with a public dataset of paired natural images with and without raindrops and the trained models are evaluated their performance of raindrop removal and background information recovery of rainwater distortion of coastal wave video imagery. In order to improve the performance, we have acquired paired video dataset with and without raindrops at the real coast and conducted transfer learning to the pre-trained models with those new dataset. The performance of fine-tuned models is improved by comparing the results from pre-trained models. The performance is evaluated using the peak signal-to-noise ratio and structural similarity index and the fine-tuned Pix2Pix network by transfer learning shows the best performance to reconstruct distorted coastal wave video imagery by raindrops.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.640-655
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• 2014

Many of today's video systems have additional depth camera to provide extra features such as 3D support. Thanks to these changes made in multimedia system, it is now much easier to obtain depth information of the video. Depth information can be used in various areas such as object classification, background area recognition, and so on. With depth information, we can achieve even higher coding efficiency compared to only using conventional method. Thus, in this paper, we propose the 2D video coding algorithm which uses depth information on top of the next generation 2D video codec HEVC. Background area can be recognized with depth information and by performing HEVC with it, coding complexity can be reduced. If current CU is background area, we propose the following three methods, 1) Earlier stop split structure of CU with PU SKIP mode, 2) Limiting split structure of CU with CU information in temporal position, 3) Limiting the range of motion searching. We implement our proposal using HEVC HM 12.0 reference software. With these methods results shows that encoding complexity is reduced more than 40% with only 0.5% BD-Bitrate loss. Especially, in case of video acquired through the Kinect developed by Microsoft Corp., encoding complexity is reduced by max 53% without a loss of quality. So, it is expected that these techniques can apply real-time online communication, mobile or handheld video service and so on.

• English Language & Literature Teaching
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• v.13 no.3
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• pp.253-273
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• 2007

This study explores issues on how students' variables are related to their educational achievement in English subject. The National Assessment of Educational Achievement (NAEA) is conduced every year to measure educational progress and achievement, and to monitor the quality of education at the national level and appropriateness of the national curriculum. It also serves the purpose to collect background information affecting educational achievement. The background information is gathered by using questionnaires for students, teachers and school administrators. Among the student variables in the national level educational achievement assessment is self-regulated learning, which is composed of self-efficacy, self-control and learning strategy. In the NAEA in 2005 it was found that the features of self-regulated learning were significantly correlated to test scores in English. The findings from the analysis of the trends of the relationships between test scores in English and information on students' self-regulated learning provide implications for the national curriculum as well as for learning and teaching.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11a
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• pp.49-52
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• 2003

본 논문에서는 실시간 감시 시스템의 응용분야를 위한 백그라운드 모델링과 업데이트 그리고 오브젝트 추출 시스템을 설계 구현한다. 일반적인 감시 시스템은 백그라운드의 모델링(background modeling)과 오브젝트의 검출(object detection), 오브젝트의 추적(tracking)으로 구성된다. 실시간 감시시스템을 가능하게 하기 위해서는 작은 시간 복잡도(low time complexity)로 백그라운드와 오브젝트를 검출할 수 있어야 하고 실외환경(outdoor)의 노이즈(noise)를 반영할 수 있어야 한다. 기존에는 빠른 백그라운드 모델링을 위해 분산, 평균, 최빈값 등을 사용한 연구들이 있었다. 이러한 방법들은 빠른 수행 속도를 보장하지만 노이즈를 오브젝트로 검출하는 문제점이 있다. 또 다른 연구 분야인 메디안(median) 검출 방법은 실외환경에 존재하는 노이즈 반영에 적합한 반면, 정렬(sorting) 연산에 많은 시간이 소요된다. 본 논문은 윈도우(Window) 기반의 러닝 윈도우 리스트(Running Window List)를 이용하여 메디안 정렬 시간을 최소화하고 실시간으로 백그라운드 모델링, 오브젝트 검출, 백그라운드 업데이트를 할 수 있는 방법을 제안한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.16-22
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• 2011

This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.

• Journal of Information Technology Applications and Management
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• v.18 no.3
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• pp.1-30
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• 2011

InnovVizwas designed and developed anew as avisualization and simulationtool to present innovation and strategy information. The InnovViz system employs two key types of technology, namely mixed reality (MR) and neural network (NN). An experiment was conducted to examine the usability, acceptance and possible adoption of this new system. Participants comprised 4 experts from 4 top performing entrepreneurial firms and 161 master degree students from 2 leading universities. The study used a modified UTAUT model and a cognition and perception model. The results revealed that when the InnovViz was introduced, the key drivers to adoption are Facilitating Conditions (FC) and Voluntary to Use (VOL). Adequate knowledge and sufficient resources were found to strongly affect FC construct. The expert's rating of a firm's innovation and performance was more congruent with senior students with a technology-background than with a finance and accounting-background. InnovViz was seen as providing complex information with an ease of use and usefulness for showing data and assessment. Among the three types of visuals depicted by InnovViz, experts rated their usefulness in descending order as follows: Cube, Tetrahedron and Saturn. Finally, experts found backward simulation to be slightly more useful for assessment than forward simulation.

• The Journal of Korean Association of Computer Education
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• v.20 no.4
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• pp.67-75
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• 2017

This study analyzed the impact of ICT usage on elementary and secondary school students on the result of ICT literacy levels for finding out why most of students were 'normal' or 'basic'. As a result, we found out that the learning action, information searching and leisure action of student using ICT had correlation with ICT literacy level. The higher the rate using ICT for learning, information searching and leisure, the higher the ICT literacy level of student. But the higher the rate using ICT for communication, the lower the ICT literacy level of student. Accordingly, we proposed the policy not to increase simply ICT using of student, but to teach how to use ICT for learning and information searching.

• Journal of Information Technology Services
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• v.6 no.3
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• pp.241-249
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• 2007

This paper describes a real-time face tracking system using effective detector and Kalman filter. In the proposed system, an image is separated into a background and an object using a real-time updated face color for effective face detection. The face features are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. The moving face is traced with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. The proposed system sets up an initial skin color and updates a region of a skin color through a moving skin color in a real time. It is possible to remove a background which has a similar color with a skin through updating a skin color in a real time. Also, as reducing a potential-face region using a skin color, the performance is increased up to 50% when comparing to the case of extracting features from a whole region.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.25-34
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• 2017

Autoencoder is a type of deep learning method where input layer and output layer are the same, and effectively extracts and restores characteristics of input vector using constraints of hidden layer. In this paper, we propose methods of Autoencoders to remove a natural background image which is a noise to the CAPTCHA and recover only a numerical images by applying various autoencoder models to a region where one number of CAPTCHA images and a natural background are mixed. The suitability of the reconstructed image is verified by using the softmax function with the output of the autoencoder as an input. And also, we compared the proposed methods with the other method and showed that our methods are superior than others.