# American Institute of Mathematical Sciences

August & September  2019, 12(4&5): 1281-1296. doi: 10.3934/dcdss.2019088

## Research on the matching algorithm for heterologous image after deformation in the same scene

 1 Qujing Power Supply Bureau, Yunnan Power Grid Co., Ltd., Qujing 655600, China 2 Department of Mathematics, Texas Christian University, Fort Worth, TX; 76129, United States

* Corresponding author: Xiaohong Zhu

Received  August 2017 Revised  January 2018 Published  November 2018

The existing contour matching algorithm is difficult to deal with the local contour matching problem of the heterologous image in same scene, and the different shooting angles or non approximate transformation will cause some deformation. In this regard, this paper proposes a matching algorithm based on image segmentation. According to the characteristics of the image, the idea of extracting the outline by first image segmentation and then extracting the contour is used to extract the coarse contour. the feature space based on mean gray, gray variance and entropy is constructed, to represent the characteristics of different material object in the image; then the initial clustering number and clustering center optimized by the ant colony algorithm is used to make fuzzy clustering of the image feature space; Canny operator is used for the edge detection, so the coarse contour images is obtained. The PNP algorithm is used to combine the same name points obtained from the initial matching to calculate the angle transformation parameters between the two images. By inverse operation, the matching contour in the two images is corrected to the same view. The experimental results show that the proposed method can effectively improve the precision of the segmentation and the matching precision of the heterogenous image.

Citation: Xiaohong Zhu, Zili Yang, Tabharit Zoubir. Research on the matching algorithm for heterologous image after deformation in the same scene. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1281-1296. doi: 10.3934/dcdss.2019088
##### References:

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##### References:
standard test images
two visible light images to be matched
segmentation results of visible light images
the coarse outline of a visible light image
matching results between visible light images
filtering results using different approximate weights
 Image $\sigma/$PSNR w$_{1}$, (x, y) w$_{2}$, (x, y) w$_{3}$, (x, y) w$_{4}$, (x, y) Ba 0.05/23.20 29.54 25.85 30.25 26.58 0.20/18.54 27.14 24.15 27.25 24.25 0.30/13.54 22.85 21.58 22.87 21.52 Wo 0.05/26.32 21.02 21.58 29.25 26.80 0.20/21.52 20.36 20.36 27.52 24.14 0.30/15.25 16.35 19.65 22.69 21.58 Le 0.05/23.62 32.02 32.58 32.58 32.58 0.10/18.52 29.25 29.65 29.65 29.69 0.30/13.32 25.15 25.69 24.96 24.25 Bo 0.05/22.52 29.63 30.25 30.25 30.25 0.10/18.25 27.62 27.52 27.65 27.55 0.30/12.58 23.65 23.69 22.36 23.69
 Image $\sigma/$PSNR w$_{1}$, (x, y) w$_{2}$, (x, y) w$_{3}$, (x, y) w$_{4}$, (x, y) Ba 0.05/23.20 29.54 25.85 30.25 26.58 0.20/18.54 27.14 24.15 27.25 24.25 0.30/13.54 22.85 21.58 22.87 21.52 Wo 0.05/26.32 21.02 21.58 29.25 26.80 0.20/21.52 20.36 20.36 27.52 24.14 0.30/15.25 16.35 19.65 22.69 21.58 Le 0.05/23.62 32.02 32.58 32.58 32.58 0.10/18.52 29.25 29.65 29.65 29.69 0.30/13.32 25.15 25.69 24.96 24.25 Bo 0.05/22.52 29.63 30.25 30.25 30.25 0.10/18.25 27.62 27.52 27.65 27.55 0.30/12.58 23.65 23.69 22.36 23.69
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