碩士 / 國立中山大學 / 資訊工程學系研究所 / 103 / In this thesis, we presented a visual learning mechanism that is used to learn the realistic motion from real fish to generate 3D animation. Usually, biological motion is simulated by physical simulation, the results of motion do not appear realistically. Furthermore, artificial 3D animation which is built beforehand needs a huge amount of labors to design based on one’s experience. In that case, the quality of animation is susceptible to one’s experience and the quantity of motion is limited to the database. Therefore, we proposed an intelligent 3D fish animation system that uses a visual learning mechanism to learn behavior from realistic creature and an interaction mechanism to allow one to interact with the virtual fish. Two fish video sequences data from front and top views are used as inputs at the same time to derive the fish skeleton sequence. The proposed learning mechanism is used to analyze fish motion and create learning data. Fish behaviors are generated by arranging several paths according to learning data, so that the motion of virtual fish will not be limited by database. Furthermore, Kinect device is used to detect one’s gesture to obtain interaction events. Five interaction events --appearing, waving, feeding, scaring and stirring are defined to allow users to interact with the virtual 3D fish.
| Identifer | oai:union.ndltd.org:TW/103NSYS5392026 |
| Date | January 2015 |
| Creators | Jin-Kun Liao, 廖晉坤 |
| Contributors | Chung-Nan Lee, 李宗南 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 55 |
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