| Project/Area Number |
10680387
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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 |
Intelligent informatics
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
OKAMOTO Masahiro Dept. of Biochemical Engineering &Science, Kyushu Institute of Technology, Associate Professor, 情報工学部, 助教授 (40211122)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 1999: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1998: ¥2,400,000 (Direct Cost: ¥2,400,000)
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| Keywords | neural network / 3D object recognition / structural learning / Gaussian GRBF network / hand-shape change / hand's motion capture / artificial intelligence / data-glove |
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| Research Abstract |
It is usually a very difficult problem to acquisition of 3D models from images automatically. One way to overcome the need of 3D model is to exploit methods for representing objects by a collection of 2D views (2D projection) rotating 3D object. Poggio et. al. and Maruyama et al. have proposed such a view-based object-recognition method named GRBF (Generalized Radial Basis Function) network which relies on multiple 2D views instead of 3D models. This network resembles to the conventional artificial neural network, however, each unit in a hidden layer is represented by radial basis function such as Gaussian. In this paper, we have applied GRBF network to the recognition of hand shape-changes such as grasp, flap point and open, and have designed the system to capture motions of the hand with the GRBF. We show their performance by computer simulations and by using data glove. Since sequential motion consists of a lot of frames, we can pick up typical several canonical frames of the motion and these frames can be applied to the GRBF network for learning. The trained GRBF network could achieve a recognition over 84%.
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