2006 Fiscal Year Final Research Report Summary
Fully automatic modeling of image-objects out of example images
| Project/Area Number |
17500061
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
WATANABE Toshinori The University of Electro-Communications, Graduate school of Information Systems, Professor, 大学院情報システム学研究科, 教授 (10242348)
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| Co-Investigator(Kenkyū-buntansha) |
KOGA Hisashi The University of Electro-Communications, Graduate school of Information Systems, Associate Professor, 大学院情報システム学研究科, 助教授 (40361836)
YOKOYAMA Takanori The University of Electro-Communications, Graduate school of Information Systems, Assistant Professor, 大学院情報システム学研究科, 助手 (10401621)
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| Project Period (FY) |
2005 – 2006
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| Keywords | Image Understanding / Multimedia / Object structure modeling / Object behavior modeling / Data compress ion / Video / Compressed image data analysis |
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| Research Abstract |
The possibility of an automatic image-object modeling is studied and affirmative results are attained as follows.
1.Object model extraction out of still images
Suppose we can compress the original image largely by giving a new name to a set of clustered color regions, we may consider
the region set as an object. Using a few object plausibility measures side by side with this principle, we could succeed in extracting, fully automatically, structural descriptions of cartoons, faces, and playthings out of color images. The structural human model could also be extracted out of a still image made up of a few frames of a human walking video.
2.Object model extraction out of a video
Se developed an algorithm composed of, the moving object extraction by background elimination, the border curve feature vector extraction, and the novelty analysis by voting from learned vectors to the incoming vector. Novel one is stored as a new model, otherwise only a model label (= recognition result) is output. Primitive human actions, i. e., walking and nodding, etc., could be extracted and used for further recognition in online real-time mode.
3.Additional outcome of 1.
Finding a set of clustered color regions in a segmented image is one of the most important tasks in 1 above. We reduced this problem into a graph matching problem, i. e., a maximum clique problem and proposed two efficient algorithms to solve it, both exploiting graph attribute information. One uses them in the process of maximum clique search and the other uses them for original graph reduction. Both are effective, but the latter dominates the former.
4.Additional outcome of 2.
The system in 2 above requires a melted video streams. So we need the decompression of a compressed video data. We examined a new possibility of video analysis directly in a compressed data domain and succeeded to track a walking person in MPEG compressed video.
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Research Products
(8 results)
