• Journal of KIISE:Computer Systems and Theory
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• v.36 no.3
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• pp.199-209
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• 2009

In this paper, we suggest a stroke level of detail (LOD) based on a saliency density. On painter]y rendering, the stroke LOD has an advantage of making the observer concentrate on the main object and improving accuracy of expression. For the stroke LOD, it is necessary to classify the detailed and abstracted area. We divide the area on the basis of saliency distribution and the level of detailed expression is controlled based on the saliency information. 'We define that the area of which the saliency distribution is high is a major subject that an artist tries to express, it is described in detail. The area of which the saliency distribution is low is abstractly described. Each divided area has the abstraction level. And by adapting the brushes of which sizes are appropriate to each level, it is possible to express the area which needs to be expressed in details from the one which needs to be expressed abstractly.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1082-1093
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• 2011

Traditional rendering of cartoon character cannot revive the feeling of concept drawings properly. In this paper, we propose capture technology to get toon shading model from the concept drawings and with this technique, we provide a new novel system to render 3D cartoon character. Benefits of this system is to cartoonize the 3D character according to saliency to emphasize the form of 3D character and further support the sketch-based user interface for artists to edit shading by post-production. For this, we generate texture automatically by RGB color sorting algorithm to analyze color distribution and rates of selected region. In the cartoon rendering process, we use saliency as a measure to determine visual importance of each area of 3d mesh and we provide a novel cartoon rendering algorithm based on the saliency of 3D mesh. For the fine adjustments of shading style, we propose a user interface that allow the artists to freely add and delete shading to a 3D model. Finally, this paper shows the usefulness of the proposed system through user evaluation.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.983-991
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• 2019

This study aims to analyze movies which contain various stories according to the size of their shots. To achieve this, it is needed to classify dataset according to the shot size, such as extreme close-up shots, close-up shots, medium shots, full shots, and long shots. However, a typical video storytelling is mainly composed of close-up shots, medium shots, full shots, and long shots, it is not an easy task to construct an appropriate dataset for extreme close-up shots. To solve this, we propose an image cropping method based on the region of interest (ROI) detection. In this paper, we use the face detection and saliency detection to estimate the ROI. By cropping the ROI of close-up images, we generate extreme close-up images. The dataset which is enriched by proposed method is utilized to construct a model for classifying shots based on its size. The study can help to analyze the emotional changes of characters in video stories and to predict how the composition of the story changes over time. If AI is used more actively in the future in entertainment fields, it is expected to affect the automatic adjustment and creation of characters, dialogue, and image editing.