# 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:
 [1] Wei Gao and Weifan Wang, New isolated toughness condition for fractional (g, f, n)-critical graphs, Colloquium Mathematicum, 147 (2017), 55-65. doi: 10.4064/cm6713-8-2016. [2] Q. He, H. Li, M. Jin, H. Duan and Q. Zhang, New necessary and sufficient condition and algorithm for directed hamiltonian graph based on boolean determinant theory, Journal of Discrete Mathematical Sciences and Cryptography, 20 (2017), 725-745. doi: 10.1080/09720529.2016.1226618. [3] S. Hu, J. Han, X. Wei and Z. Chen, A multi-hop heterogeneous cluster-based optimization algorithm for wireless sensor networks, Wireless Networks, 21 (2015), 57-65. [4] S. K. and A. M., A power allocation algorithm for multi-tier cellular networks with heterogeneous qos and imperfect channel considerations, IEEE Transactions on Wireless Communications, 99 (2017), 1-1. [5] J. López, R. Santos, X. R. Fdez-Vidal and X. M. Pardo, Two-view line matching algorithm based on context and appearance in low-textured images, Pattern Recognition, 48 (2015), 2164-2184. [6] Y. Maeda, D. Miyazaki and S. Maekawa, Volumetric aerial three-dimensional display based on heterogeneous imaging and image plane scanning, Applied Optics, 54 (2015), 4109-4115. [7] A. N. and T. A., Multithreaded maximum flow based optimal replica selection algorithm for heterogeneous storage architectures, IEEE Transactions on Computers, 5 (2016), 1543-1557. [8] J. P. P. Nunes, B. Bijeljic and M. J. Blunt, Time-of-flight distributions and breakthrough curves in heterogeneous porous media using a pore-scale streamline tracing algorithm, Transport in Porous Media, 109 (2015), 317-336. doi: 10.1007/s11242-015-0520-y. [9] P. Paulraja and S. Kumar, Edge disjoint hamilton cycles in knodel graphs. discrete mathematics and theoretical computer science, Discrete Mathematics & Theoretical Computer Science, 17 (2016), 263-284. [10] S. Peng, Q. Hu, Y. Chen and J. Dang, Improved support vector machine algorithm for heterogeneous data, Pattern Recognition, 48 (2015), 2072-2083. [11] S. Radicke, J. U. Hahn, Q. Wang and C. Grecos, A parallel hevc intra prediction algorithm for heterogeneous cpu+gpu platforms, IEEE Transactions on Broadcasting, 62 (2016), 103-119. [12] R. Sahin and L. Peide, Some approaches to multi criteria decision making based on exponential operations of simplified neutrosophic numbers, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 32 (2017), 2083-2099. [13] Y. T. Z. and Z. Q. R., An image segmentation method of digital image matting based on self-adaptive feature weighting, Bulletin of Science and Technology, 180-183. [14] Q. W., B. T. Z., B. B. X. and et al, A method of real-time picture tracking and mapping based on surf algorithm and ransac algorithm, Computer Simulation, 76 (2016), 268-272. [15] H. D. Wan, Y. F. Lu and T. Zhang, An image quality assessment method using human visual characteristics, Journal of China Academy of Electronics and Information Technology, 6 (2015), 567-573. [16] J. Wu, C. Yuen, B. Cheng, Y. Yang, M. Wang and J. Chen, Bandwidth-efficient multipath transport protocol for quality-guaranteed real-time video over heterogeneous wireless networks, IEEE Transactions on Communications, 64 (2016), 2477-2493. [17] J. Wu, C. Yuen, N. M. Cheung and J. Chen, Delay-constrained high definition video transmission in heterogeneous wireless networks with multi-homed terminals, IEEE Transactions on Mobile Computing, 15 (2016), 641-655. [18] C. Yue, Z. Yan and W. S. G., Fast image stitching method based on sift feature vector image, Journal of Jilin University(Science Edition), 1 (2017), 116-122. [19] W. Zhao, S. Wang, C. Wang and X. Wu, Approximation algorithms for cell planning in heterogeneous networks, IEEE Transactions on Vehicular Technology, 66 (2017), 1561-1572.
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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