This study proposes a framework for an intelligent agent information service using digital human and deep learning technology. The framework can recognize the identity of individuals using facial features and provide personalized services through a digital human. The personalized service is defined by a relevance graph based on personal data collected in advance. The proposed system can continuously evolve to recommend customized services using relevance graphs and dynamic data processing, gradually become more intelligent using additionally collected data. Moreover, it uses animation keyframe interpolation for natural and seamless digital human interaction and provides visual effects that are synchronized based on specific information collected for the intuitive service. The proposed system was tested on a school domain for two months, and a statistical domain feedback system based on a mathematical model that predicts service usage per unit time was developed using the recorded information. Additionally, we evaluate our system through user experience surveys.
The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings, the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article.
This paper presents a way to generate a stained glass animation from a given video input. We first obtain low-frequency components from input video frames to get rid of textures which cause over-segmentation in image segmentation. Then we segment input video volume by employing mean-shift video segmentation. The segmented regions are too large to be architecturally stable, subdivision is required. To sub-divide regions temporally coherent, we obtain the panoramic image from the segmented regions, and sub-divide them by using weighted Voronoi diagram. To render these sub-divided regions as stained glass pieces, we find the best match glass piece in the real stained glass piece image database and transfer its color to that region. Finally, we generate lead came at the boundary of regions, which results in a temporally coherent stained glass animation.
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