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Proceedings Paper

Research on image matching method of big data image of three-dimensional reconstruction
Author(s): Chunsen Zhang; Zhenguo Qiu; Shihuan Zhu; Xiqi Wang; Xiaolei Xu; Sidong Zhong
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Paper Abstract

Image matching is the main flow of a three-dimensional reconstruction. With the development of computer processing technology, seeking the image to be matched from the large date image sets which acquired from different image formats, different scales and different locations has put forward a new request for image matching. To establish the three dimensional reconstruction based on image matching from big data images, this paper put forward a new effective matching method based on visual bag of words model. The main technologies include building the bag of words model and image matching. First, extracting the SIFT feature points from images in the database, and clustering the feature points to generate the bag of words model. We established the inverted files based on the bag of words. The inverted files can represent all images corresponding to each visual word. We performed images matching depending on the images under the same word to improve the efficiency of images matching. Finally, we took the three-dimensional model with those images. Experimental results indicate that this method is able to improve the matching efficiency, and is suitable for the requirements of large data reconstruction.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130S (14 December 2015); doi: 10.1117/12.2205871
Show Author Affiliations
Chunsen Zhang, Xi'an Univ. of Science and Technology (China)
Zhenguo Qiu, Xi'an Univ. of Science and Technology (China)
Shihuan Zhu, Xi'an Univ. of Science and Technology (China)
Xiqi Wang, Xi'an Univ. of Science and Technology (China)
Xiaolei Xu, Xi'an Univ. of Science and Technology (China)
Sidong Zhong, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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