• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1537-1545
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• 2016

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.183-192
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• 2021

This study was purposed to explore the learning culture through the related literature and research review in Jewish Havruta which has interaction, critical reflection, and the driving force creating a better world. The prior researches on Havruta mainly tend to as ways to increase learners' interest in learning and studies as curriculum or teaching methods such as creativity, understanding, and problem-solving skills. However, Havruta is not just method to study subjects, but rather a process of developing thinking through dialogue and discussion. Therefore, Havruta's essential meaning as a lifelong learning should be explored. Studies showed that Jews embody the thinking process from interpreting, analyzing, setting up logic, questioning, discussing, and debating Talmud with others anytime, anywhere, and anyone throughout their learning culture. It develops basic skills for life, forms an integrated personality in relationships with others, and continuously conducts lifelong learning to shape one's own beings. Therefore, lifelong learning culture would be sharing information that one has in the process of discussion through dialogue between two or more people, and supporting and encouraging the other's failure or fear rather than attacking them. The embodiment of thinking process in which people teach and learn eachother, accept the difference, and expand thought would be significant foundation to create lifelong learning culture.

• Journal of The Korean Association For Science Education
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• v.8 no.2
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• pp.59-64
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• 1988

• The Mathematical Education
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• v.31 no.3
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• pp.29-42
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• 1992

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.74-77
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• 2021

최근 딥러닝을 이용하여 객체 인식 학습을 위한 데이터셋을 구축하는데 있어 시간과 인력을 단축하기 위해 인조 데이터를 생성하는 연구가 진행되고 있다. 하지만 실제 환경과 관계없이 임의의 배경에 배치되어 구축된 데이터셋으로 학습된 네트워크를 실제 환경으로 구성된 데이터셋으로 테스트할 경우 인식률이 저조하다. 이에 본 논문에서는 실제 배경 이미지에 객체 이미지를 합성하고, 다양성을 위해 3차원으로 회전하여 증강하는 인조 데이터셋 생성 시스템을 제안한다. 제안된 방법으로 구축된 인조 데이터셋으로 학습한 네트워크와 실제 데이터셋으로 학습된 네트워크의 인식률을 비교한 결과, 인조 데이터셋의 성능이 실제 데이터셋의 성능보다 2% 낮았지만, 인조 데이터셋을 구축하는 시간이 실제 데이터셋을 구축하는 시간보다 약 11배 빨라 시간적으로 효율적인 데이터셋 구축 시스템임을 증명하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.446-450
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• 2020

딥러닝 기반의 영상 분석 방법들은 많은 양의 학습 데이터가 필요하며, 학습 데이터 구축에는 많은 시간과 노력이 소요된다. 특히 객체 검출 분야의 경우 영상 내 객체의 위치, 크기, 범주 등의 정보가 모두 필요하여 학습 데이터 구축에 더 많은 어려움이 있으며, 이를 해결하기 위해 최근 이미지 합성기반 데이터 증강에 관한 연구가 활발히 진행되고 있다. 이미지 합성기반 데이터 증강 방법은 배경 영상에 객체를 합성할 때 객체와 배경 영상이 접한 영역에서 아티팩트(Artifact)가 발생하며, 이는 객체 검출 모델이 아티팩트를 객체의 특징으로 모델링하여 검출 성능이 저하되는 원인이 된다. 이러한 문제를 해결하기 위하여 본 논문에서는 양방향 필터 기반의 이미지 합성 방법을 제안하고, 단일 단계 검출의 대표적인 방법인 RetinaNet을 이용하여 이미지 합성기반 데이터 증강 방법의 성능을 분석하였다. 공개 데이터셋에 대한 실험 결과 본 논문에서 사용한 단일 검출 방법 및 데이터 증강 기법을 사용하면 더 적은 양의 증강 데이터로 기존 방법과 동일한 성능을 보여주는 것을 확인하였다.

• Journal of the Korean Geographical Society
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• v.39 no.6 s.105
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• pp.971-983
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• 2004

It is increasingly clear that student mastery of concepts and skills in geographic education is based on a complex set of variables. Sex and gender are the key variables. Much has been written about biological sex differences in learning, but less attention has been paid to the impacts of socio-cultural gender on learning geography. As such, the aims of this paper are two-fold. First, to examine theories which seek to explain why males and females might differ in their geographic and spatial knowledge or skill. Second, to examine the extent of sex differences and gender traits in the geographic learning. The results of study illustrate clearly that there are more similarities than differences between the sexes. Therefore, there are significant gender differences between the preferences of regions, contents, activities in the secondary geographic learning. The results also provide insights into improving contents and method of geographic education.

• Journal of the Korean School Mathematics Society
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• v.15 no.3
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• pp.487-509
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• 2012

This study, in order to contribute to improvement of the affective domain for mathematics which is one of the largest issues of mathematics education, examined the background variables influencing mathematics anxiety of middle/high school students. As the result, the middle school students showed a greater level of anxiety than the high school students did, and especially the anxiety level according to environment factor and learning strategy factor was high. Also, male students showed overall a greater mathematics anxiety than female students did, and both group of students showed a higher anxiety level according to environment factor and learning strategy factor. Besides, the greater the time spent on private education was, the higher the mathematics anxiety level, and in test/performance factor, the group with low self-initiated learning time showed a higher level of mathematics anxiety compared to other two groups. Among four subfactors of mathematics anxiety test, the students overall scored lowest in anxiety for test/performance factor, and highest in environment factor.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.1055-1060
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• 2007

최근 사회적 소프트웨어(Social Software)의 급격한 발전은 미래의 지식근로자가 될 넷제너레이션의 학습 방법에 큰 영향을 줄 것임에 틀림없다. 이러한 변화에 맞추어 대학 교육에서도 오늘날의 학생들이 지식을 창출하고 공유하는 경험을 하게 하는 사회적 소프트웨어를 교육에 활용하는 교수법이 확산 ㄷ히고 있다. 구체적으로 블로그(Blog)와 위키(Wiki)와 같은 사회적 소프트웨어를 사용하는 교육 방법에 대한 관심이 높아지고 있다. 본 논문에서는 위키(Wiki), 블로그(Blog)와 같은 사회적 소프트웨어를 사용하여 실시하는 협업 학습(Collaborative Learning)의 이론적 배경과 운영 경험을 소개한다.