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- 저자 : Kuniaki Uehara (검색결과 2 건)
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A Novel Topic Extraction Method based on Bursts in Videos Streams
Kimiaki Shirahama,Kuniaki Uehara 보안공학연구지원센터 2008 International Journal of Hybrid Information Techno Vol.1 No.3
In this paper, we introduce a novel method for extracting “topics” as interesting events in a video. Here, we define the interestingness of an event by the anomaly of a target character’s appearance and disappearance pattern. As examples of abnormal patterns, shot durations in thrilling events are very short while shot durations in romantic events are very long. In contrast, as an example of non-abnormal pattern, conversation events are presented by the pattern, where the target character repeatedly appears in one shot and then another character appears in the next shot. From the above point of view, our topic extraction method aims to detect the following two types of abnormal patterns, called “bursts”. The first type of burst is a pattern where the target character appears in shots with very short durations, while the second is a pattern where he/she appears in shots with very long durations. To detect such bursts, we firstly divide the video into events characterized by specific patterns of the target character’s ppearance and disappearance. We locate these patterns in the video by using time series segmentation technique. Then, we extract topics by examining whether the pattern in each event can be regarded as a burst or not. Experiments on different videos validate that a character's ppearance and disappearance patterns are effective for obtaining semantically meaningful events. And, bursts are useful for extracting many interesting topics.
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3D Face Reconstruction from a Single Image Using Machine Learning Methodology
Yoshihito Mori,Yoshiaki Yasumura,Kuniaki Uehara 한국멀티미디어학회 2009 한국멀티미디어학회 국제학술대회 Vol.2009 No.-
This paper presents a method for 3D face reconstruction from a single image. To reconstruct a 3D-shape from a single image, we need to model the relationship between images and shapes. The relationship defines a function from image brightness variation to local shape variation. Some existing methods for 3D reconstruction assume a series of reflectance models that try to emulate the relationship based on physical theoretics. However, the models of the existing methods are not able to fit actual world enough. Therefore, we propose heuristic approach, which empirically learns the relationship by statistical machine learning methodology. Firstly, we create surface normal estimators by learning the relationship between image patches of brightness and true surface normals. However, these estimators make misestimation in some cases. To correct them, we additionally create surface normal correctors by learning from image patches, estimated normal patches, and true normals. We conducted some experiments for evaluating this method. These results showed efficiency of our approach.
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