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Proceedings Paper

A line mapping based automatic registration algorithm of infrared and visible images
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Paper Abstract

There exist complex gray mapping relationships among infrared and visible images because of the different imaging mechanisms. The difficulty of infrared and visible image registration is to find a reasonable similarity definition. In this paper, we develop a novel image similarity called implicit line segment similarity(ILS) and a registration algorithm of infrared and visible images based on ILS. Essentially, the algorithm achieves image registration by aligning the corresponding line segment features in two images. First, we extract line segment features and record their coordinate positions in one of the images, and map these line segments into the second image based on the geometric transformation model. Then we iteratively maximize the degree of similarity between the line segment features and correspondence regions in the second image to obtain the model parameters. The advantage of doing this is no need directly measuring the gray similarity between the two images. We adopt a multi-resolution analysis method to calculate the model parameters from coarse to fine on Gaussian scale space. The geometric transformation parameters are finally obtained by the improved Powell algorithm. Comparative experiments demonstrate that the proposed algorithm can effectively achieve the automatic registration for infrared and visible images, and under considerable accuracy it makes a more significant improvement on computational efficiency and anti-noise ability than previously proposed algorithms.

Paper Details

Date Published: 11 September 2013
PDF: 9 pages
Proc. SPIE 8907, International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications, 89072J (11 September 2013); doi: 10.1117/12.2033047
Show Author Affiliations
Rui Ai, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)
Zelin Shi, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Dejiang Xu, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)
Chengshuo Zhang, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)


Published in SPIE Proceedings Vol. 8907:
International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications
Haimei Gong; Zelin Shi; Qian Chen; Jin Lu, Editor(s)

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