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Proceedings Paper

3D reconstruction approach in neurovision system
Author(s): Yingen Xiong; Haifeng Hu
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Paper Abstract

In this paper, a new 3D reconstruction approach for 3D object recognition in neuro-vision system is presented. First, a phase based stereo matching using Hopfield neural network approach is presented. The stereo matching problems are treated in frequency domain by using local phase, instead of matching feature or texture of images, the stereo matching process is performed by using local phases of left image and right image in stereo image pair. By using the windowed Fourier transform, the windowed Fourier phases can be calculated. Through the variable window Gabor filter, the local phases of image can also be obtained. The Hopfield neural network is adopted to implement the stereo matching process. A suitable architecture of neural network is established, so that the computation can be implemented efficiently in parallel. A suitable matching function is created by using the local phase property. The energy function for neural network is constructed with satisfying some necessary constraints. The stereo matching process then is carried to find the minimum energy corresponding to the solution of the problem. Second, a 3D object reconstruction neural network is constructed by using BP neural network. So the 3D configuration and shape can be reconstructed by this neural network. With multiple neural networks the 3D Reconstruction processes can be performed in parallel. The examples for both synthetic and real images are shown in the experiment, and good results are obtained.

Paper Details

Date Published: 9 October 2000
PDF: 12 pages
Proc. SPIE 4221, Optical Measurement and Nondestructive Testing: Techniques and Applications, (9 October 2000); doi: 10.1117/12.402650
Show Author Affiliations
Yingen Xiong, Zhongshan Univ. (United States)
Haifeng Hu, Zhongshan Univ. (China)


Published in SPIE Proceedings Vol. 4221:
Optical Measurement and Nondestructive Testing: Techniques and Applications
FeiJun Song; Frank Chen; Michael Y.Y. Hung; H.M. Shang, Editor(s)

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