• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.218-222
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• 2006

In making 3D animation with digital technology, it is necessary to increase productivity and reusability by managing production pipeline systematically through standardization of animation content. For this purpose, we try to develop the animation content management system that can manage all kind of information on the production pipeline, based on SCORM of e-teaming by considering production, publication and re-editing. A scene as the unit of visual semantics is standardize into an object that contains meta-data of place, cast, weather, season, time and viewpoint about the scene. The meta-data of content includes a lot of information of copyright, publication, description, etc, so that it plays an important role on the management and the publication. If an effective management system of meta-data such as ontology will be implemented, it is possible to search multimedia contents powerfully. Hence, it will bring on production and publication of UCC. Using the meta-data of content object, user and producer can easily search and reuse the contents. Hence, they can choose the contents object according to their preference and reproduce their own creative animation by reorganizing and packaging the selected objects.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.69-79
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• 2008

Due to popularization of the wireless Internet and mobile devices the infrastructure of the ubiquitous environment, where users can get information whatever they want anytime and anywhere, is created. Therefore, a variety of fields including the education studies methods for efficiency of information transmission using on-line and off-line contents. In this paper, we propose the Mixed Mobile Education system(MME) that improves educational efficiency using on-line and off-line contents on mobile devices. Because it is hard to input new data and cannot use similar off-line contents in systems used additional tags, the proposed system does not use additional tags but recognizes of-line contents as we extract feature points in the input image using the mobile camera. We use the Scale Invariant Feature Transform(SIFT) algorithm to extract feature points which are not affected by noise, color distortion, size and rotation in the input image captured by the low resolution camera. And we use the client-server architecture for solving the limited storage size of the mobile devices and for easily registration and modification of data. Experimental results show that compared with previous work, the proposed system has some advantages and disadvantages and that the proposed system has good efficiency on various environments.

• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.23-32
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• 2006

In recent years, since the digital camera has been popularized, so users can easily collect hundreds of photos in a single usage. Thus the managing of hundreds of digital photos is not a simple job comparing to the keeping paper photos. We know that managing and classifying a number of digital photo files are burdensome and annoying sometimes. So people hope to use an automated system for managing digital photos especially for their own purposes. The previous studies, e.g. content-based image retrieval, were focused on the clustering of general images, which it is not to be applied on digital photo clustering and classification. Recently, some specialized clustering algorithms for images clustering digital camera images were proposed. These algorithms exploit mainly the statistics of time gap between sequent photos. Though they showed a quite good result in image clustering for digital cameras, still lots of improvements are remained and unsolved. For example the current tools ignore completely the image transformation with the different focal lengths. In this paper, we present a photo considering focal length information recorded in EXIF. We propose an algorithms based on MVA(Matching Vector Analysis) for classification of digital images taken in the every day activity. Our experiment shows that our algorithm gives more than 95% success rates, which is competitive among all available methods in terms of sensitivity, specificity and flexibility.