• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1139-1149
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• 2006

This paper describes a novel and efficient algorithm, which extracts focused objects from still images with low depth-of-field (DOF). The algorithm unfolds into four modules. In the first module, a HOS map, in which the spatial distribution of the high-frequency components is represented, is obtained from an input low DOF image [1]. The second module finds OOI candidate by using characteristics of the HOS. Since it is possible to contain some holes in the region, the third module detects and fills them. In order to obtain an OOI, the last module gets rid of background pixels in the OOI candidate. The experimental results show that the proposed method is highly useful in various applications, such as image indexing for content-based retrieval from huge amounts of image database, image analysis for digital cameras, and video analysis for virtual reality, immersive video system, photo-realistic video scene generation and video indexing system.

• Journal of the Korean Society for information Management
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• v.29 no.4
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• pp.123-142
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• 2012

To develop an intelligent search engine to help users retrieve information effectively, various methods, such as Semantic Web, have been used, An effective retrieval method of such methods uses ontology technology. In this paper, we built National R&D ontology after analyzing National R&D Information in NTIS and then implemented National R&D Knowledge Map to represent and retrieve information of the relationship between object and subject (project, human information, organization, research result) in R&D Ontology. In the National R&D Knowledge Map, center-node is the object selected by users, node is subject, subject's sub-node is user's favorite query in National R&D ontology after analyzing the relationship between object and subject. When a user selects sub-node, the system displays the results from inference engine after making query by SPARQL in National R&D ontology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.752-758
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• 2012

SNS(Social Networking Services) is oneline service that enable users to construct human network through their relation on web, such as following relation, friend relation, and etc. Recently, owing to the advent of digital devices (smart phone, tablet PC) which embedded GPS some applications which provide services with spatial relevance and social relevance have been released. Such an online service is called LBSNS. It is required to use spatial filtering so as to build the LBSNS system that enable users to subscribe information of interesting area. For spatial filtering, user and tweet attaches location information which divide into static property presenting fixed area and dynamic property presenting user's area changed along the moving user. In the case of using a location information including dynamic property, Continuous query occurred from the moving user causes the problem in server. In this paper, we propose spatial filtering algorithm using Virtual Grid for reducing frequency of query, and conclude that frequency of query on using Virtual Grid is 93% decreased than frequency of query on not using Virtual Grid.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.990-999
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• 2007

In this paper, we propose the region-based image retrieval method using JSEG which is a method for unsupervised segmentation of color-texture regions. JSEG is an algorithm that discretizes an image by color classification, makes the J-image by applying a region to window mask, and then segments the image by using a region growing and merging. The segmented image from JSEG is given to a user as the query image, and a user can select a few segmented regions as the query region. After finding the MBR of regions selected by user query and generating the multiple window masks based on the center point of MBR, we extract the feature vectors from selected regions. We use the accumulated histogram as the global descriptor for performance comparison of extracted feature vectors in each method. Our approach fast and accurately supplies the relevant images for the given query, as the feature vectors extracted from specific regions and global regions are simultaneously applied to image retrieval. Experimental evidence suggests that our algorithm outperforms the recent image-based methods for image indexing and retrieval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.851-856
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• 2015

As crime has actually increased in recent years, various mobile applications related to safety and emergency measure have received much attention. Therefore, IoT (Internet of Things) technologies, which connect various physical objects with Internet communication, have been also paid attention and then diverse safety services based on IoT technologies have been on the increase. However, existing mobile safety applications are simply based on location based service (LBS). Also, as they are independently operated without the help of another safety systems, they cannot efficiently cope with various safety situations. So, this paper proposes the efficient smart safety service architecture with both the risky situation detection using user location as well as various sensing information and the risk congruence measure using the streetlight infrastructure. Additionally, UDID (unique device identifier) is utilized for the secure communication with the control center.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.193-198
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• 2023

We study a learner-customized lecture recommendation project using deep learning. Recommendation systems can be easily found on the web and apps, and examples using this feature include recommending feature videos by clicking users and advertising items in areas of interest to users on SNS. In this study, the sentence similarity Word2Vec was mainly used to filter twice, and the course was recommended through the Surprise library. With this system, it provides users with the desired classification of course data conveniently and conveniently. Surprise Library is a Python scikit-learn-based library that is conveniently used in recommendation systems. By analyzing the data, the system is implemented at a high speed, and deeper learning is used to implement more precise results through course steps. When a user enters a keyword of interest, similarity between the keyword and the course title is executed, and similarity with the extracted video data and voice text is executed, and the highest ranking video data is recommended through the Surprise Library.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.3
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• pp.165-178
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• 2007

The recent progress in multimedia signal processing and transmission technologies has contributed to the extensive use of multimedia devices to watch sports games with small LCD panel. However, the most of video sequences are captured for normal viewing on standard TV or HDTV, for cost reasons, merely resized and delivered without additional editing. This may give the small-display-viewers uncomfortable experiences in understanding what is happening in a scene. For instance, in a soccer video sequence taken by a long-shot camera techniques, the tiny objects (e.g., soccer ball and players) may not be clearly viewed on the small LCD panel. Moreover, it is also difficult to recognize the contents of the scorebox which contains the elapsed time and scores. This renuires intelligent display technique to provide small-display-viewers with better experience. To this end, one of the key technologies is to determine region of interest (ROI) and display the magnified ROI on the screen, where ROI is a part of the scene that viewers pay more attention to than other regions. Examples include a region surrounding a ball in long-shot and a scorebox located in the comer of each frame. In this paper, we propose a scheme for raising viewing experiences of multimedia mobile device users. Instead of taking generic approaches utilizing visually salient features for extraction of ROI in a scene, we take domain-specific approach to exploit unique attributes of the soccer video. The proposed scheme consists of two modules: ROI determination and scorebox extraction. The experimental results show that the proposed scheme offers useful tools for intelligent video display on multimedia mobile devices.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.1024-1026
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• 2005

P2P 네트워크는 사용자에게 보다 효율적으로 않은 자원을 공유하고 사용할 수 있는 방법을 제공해야 한다. 이 논문에서는 Ultrapeer와 동일한 관심을 갖는 peer들을 서로 그룹화 함으로써 검색에 필요한 query의 수를 줄이는 방법을 제안한다. 이 방법을 검증하기 위해 세가지 P2P 네트워크-비구조적이며 브로드케스팅으로 검색하는 네트워크, ultrapeer가 존재하며 지역적 특성을 그룹화 기준으로 하는 네트워크, ultrapeer가 존재하며 동일한 관심을 그룹의 기준으로 하는 네트워크로 모델링 하고 각 모델별로 필요한 데이터의 검색과 전송을 시뮬레이션하여 검색 시간과 발생한 query의 수를 비교하여 제시한다.

• Information and Communications Magazine
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• v.33 no.2
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• pp.59-65
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• 2016

최근 급격하게 확산되고 있는 핀테크 서비스는 다양한 분야의 사람들로부터 관심을 받고 있다. 기존 금융거래 프로세스에서 경험했던 불편함과 비효율을 개선하여 소비자와 기업 모두에게 편리성과 비용절감이라는 혜택을 제공하고, 새롭게 재편되고 있는 금융 산업에 참여할 기회를 제공하기 때문이다. 그러나 핀테크 서비스가 가져다 줄 혜택과 기회는 완벽한 보안에 기반하지 않으면 엄청난 피해를 야기할 수 있다는 우려도 존재한다. 본고에서는 핀테크 보안 기술 중 최근 급격히 관심을 받고 있는 FIDO (Fast IDentity Online) 인증 기술에 대해 살펴보고자 한다. 편의성과 보안성 측면에서 한계를 갖고 있던 기존 인증 기술들이 핀테크 서비스를 확산시키는데 장애가 되었다면, 최근 도입되기 시작한 FIDO 기술은 편리하고 강력한 인증을 제공하여 사용자와 기업 모두의 관심을 얻는데 성공하고 있는 것으로 보인다. 본 고에서는 FIDO 기술을 간단히 설명하고, FIDO 기술을 활용한 응용 보안 기술을 소개하고자 한다. 또한 FIDO 기술의 향후 발전 방향에 대해 현재 진행 중인 표준화 내용을 중심으로 살펴보고, 해외에서 활발히 진행되고 있는 연구들을 통해 핀테크 인증 기술의 발전 방향을 전망하고 결론을 맺는다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.769-771
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• 2004

요즘 Web-Browsing, 영상 데이터 베이스 그리고 원격 진료와 같은 여러 응용 분야에서는 압축할 이미지내의 사용자의 관심 영역을 다른 영역보다 더 우선적으로 처리할 필요가 있다. 즉, 영상을 전송하는데 있어서 관심영역(ROI ： Region Of Interest)을 먼저 전송하고, 영상 복원 시에도 영상의 전체 영역 중 ROI 영역이 우선적으로 복원하여야 하는 경우가 있다. Maxshift 방법은 JPEG2000 ROI Coding 에서 표준으로 사용하고 있다. 그러나 Maxshift 방법은 단지 하나의 ROI 영역만을 처리 가능하다. 본 논문에서는 기존의 방법을 이용하여 우선 순위를 가지는 Multiple ROI Coding 기법을 제안한다. 제안한 방법에서는 계수값들의 비트 플레인에 대한 스케일링 변수를 이용하여 우선 순위를 가지는 Multiple ROI 부호화가 가능함을 보이고, 저 비트율에서 Maxshift 방법보다 좀 더 우수한 성능을 확인하였다.