• Electronics and Telecommunications Trends
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• v.27 no.6
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• pp.21-30
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• 2012

디지털 홀로그래픽 프린팅(Digital Holographic Printing: DHP)은 종래의 아날로그 방식이 아닌 디지털 기록 방식으로 이미지 또는 간섭무늬를 홀로그래픽 매질에 기록하여 정지 화상에 대한 홀로그램 상을 재생하는 홀로그래픽 기술이다. 현재 개발 중인 스테레오그램(stereogram) 기반 DHP는 수직/수평 방향의 시차(parallax)를 갖는 초다시점 영상을 매질에 촘촘히 기록하여 마치 아날로그 홀로그램과 같이 자연스럽게 3차원 상을 재현하는 기술이다. 하지만 이러한 기술은 과도기적인 성격의 기술로 향후에는 위상 정보를 포함하고 있는 홀로그램인 프린지 패턴(fringe pattern)을 직접 기록함으로써 보다 더 자연스러운 형태의 홀로그램 상을 재생할 수 있는 기술이 추가적으로 개발될 것으로 예상된다. 그러므로 본고에서는 Zebra Imaging사에서 개발한 스테레오그램 기반의 DHP 기술과 Nihon 대학이 시도한 프린지 기반의 DHP 기술 동향을 다루고자 한다.

• Journal of Korean Society of Archives and Records Management
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• v.9 no.1
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• pp.197-219
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• 2009

Photographic records are very useful and meaningful in the point of view that users can understand those things easily. However, there are few case of digitalizing photographic records, compared to printed records because of high cost and management limitation. The purpose of this study is to provide improvement plan and consideration for digitalizing analog photographic records. To achieve the goals, this study analyzes management status of photographic records in 16 provincial offices. Also, this study performs a case study on digitalization of analog photographic records and implementation of DB relating digitalized photographic records in J provincial office. Based on the results, this study proposes how to digitalize analog photographic records, and manage of digitalized photographic records.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.541-543
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• 2001

본 연구에서는 1비트 단위로 연속적으로 입력되는 수표의 영상데이터를 실시간으로 압축처리하고 또한 수표의 하단부에 기록된 인식하기 위한 알고리즘과 하드웨어 구현을 보여준다. 제안된 알고리즘에서는 실시간 처리를 위해 하드웨어에 적합한 알고리즘이 소개되며, 실제로 PLD로 설계 구현하여 그 타당성을 확인하였다.

• Digital Contents
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• no.5 s.132
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• pp.72-73
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• 2004

한국정보문화진흥원이 운영하는 정보통신체험관‘IT월드’(과천 서울대공원내)에서 지난 4월 3일부터 11일까지 미국 자연사박물관, 영국 내셔널 스페이스센터, 스페인 아일라 메지카 등 선진 체험학습관에서 제작해 선보여 왔던‘세계 디지털 돔영상 페스타’를 개최해 가족단위 관람객들이 몰려 매회 매진사례를 기록했다.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.724-733
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• 2017

Conventional methods of automatically recording Go game positions do not properly consider obstacles (hand or object) on a Go board during the Go game. If the Go board is blocked by obstacles, the position of a Go stone may not be correctly recognized, or the sequences of moves may be stored differently from the actual one. In the proposed algorithm, only the complete Go board image without obstacles is stored as a background image and the obstacle is recognized by comparing the background image with the current input image. To eliminate the phenomenon that the shadow is mistaken as obstacles, this paper proposes the new obstacle detection method based on the gradient image instead of the simple differential image. When there is no obstacle on the Go board, the background image is updated. Finally, the successive background images are compared to recognize the position and type of the Go stone. Experimental results show that the proposed algorithm has more than 95% recognition rate in general illumination environment.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.31-37
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• 2021

In this paper, we propose a method to effectively search by automatically classifying scenes in which specific images such as pitching or swing appear in baseball game images using a CNN(Convolution Neural Network) model. In addition, we propose a video scene search system that links the classification results of specific motions and game records. In order to test the efficiency of the proposed system, an experiment was conducted to classify the Korean professional baseball game videos from 2018 to 2019 by specific scenes. In an experiment to classify pitching scenes in baseball game images, the accuracy was about 90% for each game. And in the video scene search experiment linking the game record by extracting the scoreboard included in the game video, the accuracy was about 80% for each game. It is expected that the results of this study can be used effectively to establish strategies for improving performance by systematically analyzing past game images in Korean professional baseball games.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.37-45
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• 2022

As of 2020, about 500 hours of videos are uploaded to YouTube, a representative online video platform, per minute. As the number of users acquiring information through various uploaded videos is increasing, online video platforms are making efforts to provide better recommendation services. The currently used recommendation service recommends videos to users based on the user's viewing history, which is not a good way to recommend videos that deal with specific purposes and interests, such as educational videos. The recent recommendation system utilizes not only the user's viewing history but also the content features of the item. In this paper, we extract the content features of educational video for educational video recommendation based on YouTube, design a recommendation system using it, and implement it as a web application. By examining the satisfaction of users, recommendataion performance and convenience performance are shown as 85.36% and 87.80%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

• Journal of Korean Electronics
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• v.5 no.11
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• pp.77-85
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• 1985

• Journal of Korean Society of Archives and Records Management
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• v.22 no.4
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• pp.129-149
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• 2022

Records have temporal characteristics, including the past and present; linguistic characteristics not limited to a specific language; and various types categorized in a complex way. Processing records such as text, video, and audio in the life cycle of records' creation, preservation, and utilization entails exhaustive effort and cost. Primary natural language processing (NLP) technologies, such as machine translation, document summarization, named-entity recognition, and image recognition, can be widely applied to electronic records and analog digitization. In particular, Korean deep learning-based NLP technologies effectively recognize various record types and generate record management metadata. This paper provides an overview of Korean NLP technologies and discusses considerations for applying NLP technology in records management. The process of using NLP technologies, such as machine translation and optical character recognition for digital conversion of records, is introduced as an example implemented in the Python environment. In contrast, a plan to improve environmental factors and record digitization guidelines for applying NLP technology in the records management field is proposed for utilizing NLP technology.