| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 7 | |
| Section | 3D Images Reconstruction and Virtual System | |
| DOI | https://doi.org/10.1051/matecconf/201823202002 | |
| Published online | 19 November 2018 | |
Simulation of 3D Image Reconstruction in Rigid body Motion
1
Chen Huihong (School of information engineering, Guangzhou Panyu polytechnic University, Guangzhou China, 511483 )
2*
Liu Shiming (Corresponding author) (School of management,Guangzhou Panyu polytechnic University, GuangZhou China, 511483 )
* Corresponding author: 18520576074, 31341517@qq.com/chenjin_2002@163.com 18520089909, hero640@163.com
3D image reconstruction under rigid body motion is affected by rigid body motion and visual displacement factors, which leads to low quality of 3D image reconstruction and more noise, in order to improve the quality of 3D image reconstruction of rigid body motion. A 3D image reconstruction technique is proposed based on corner detection and edge contour feature extraction in this paper. Region scanning and point scanning are combined to scan rigid body moving object image. The wavelet denoising method is used to reduce the noise of the 3D image. The edge contour feature of the image is extracted. The sparse edge pixel fusion method is used to decompose the feature of the 3D image under the rigid body motion. The irregular triangulation method is used to extract and reconstruct the information features of the rigid body 3D images. The reconstructed feature points are accurately calibrated with the corner detection method to realize the effective reconstruction of the 3D images. The simulation results show that the method has good quality, high SNR of output image and high registration rate of feature points of image reconstruction, and proposed method has good performance of 3D image reconstruction.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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