A rotation invariant template matching algorithm based on Sub-NCC

Yifan Zhang; Zhi Zhang; Shaohu Peng; Dongyuan Li; Hongxin Xiao; Chao Tang; Runqing Miao; Lingxi Peng; Yifan Zhang; Zhi Zhang; Shaohu Peng; Dongyuan Li; Hongxin Xiao; Chao Tang; Runqing Miao; Lingxi Peng

doi:10.3934/mbe.2022442

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 9: 9505-9519. doi: 10.3934/mbe.2022442

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A rotation invariant template matching algorithm based on Sub-NCC

1.
School of Electronic and Communication Engineering, Guangzhou University, Guangzhou 511400, China
2.
School of Economics and Statistics, Guangzhou University, Guangzhou 511400, China
3.
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 511400, China
4.
Mathematics and Applied Mathematics College of Arts and Sciences, Beijing Normal University, Zhuhai 519085, China

† The authors contributed equally to the first author.
Academic Editor: Runzhang Xu

Received: 28 April 2022 Revised: 15 June 2022 Accepted: 22 June 2022 Published: 29 June 2022

This paper proposes an anti-rotation template matching method based on a portion of the whole pixels. To solve the problem that the speed of the original template matching method based on NCC (Normalized cross correlation) is too slow for the rotated image, a template matching method based on Sub-NCC is proposed, which improves the anti-jamming ability of the algorithm. At the same time, in order to improve the matching speed, the rotation invariant edge points are selected from the rotation invariant pixels, and the selected points are used for rough matching to quickly screen out the unmatched areas. The theoretical analysis and experimental results show that the accuracy of this method is more than 95%. For the search map at any angle with the resolution at the level of 300,000 pixel, after selecting the appropriate pyramid series and threshold, the matching time can be controlled to within 0.1 s.
- machine vision,
- template matching,
- rotation invariance,
- stable pixels,
- normalized cross correlation
Citation: Yifan Zhang, Zhi Zhang, Shaohu Peng, Dongyuan Li, Hongxin Xiao, Chao Tang, Runqing Miao, Lingxi Peng. A rotation invariant template matching algorithm based on Sub-NCC[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 9505-9519. doi: 10.3934/mbe.2022442

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Abstract

This paper proposes an anti-rotation template matching method based on a portion of the whole pixels. To solve the problem that the speed of the original template matching method based on NCC (Normalized cross correlation) is too slow for the rotated image, a template matching method based on Sub-NCC is proposed, which improves the anti-jamming ability of the algorithm. At the same time, in order to improve the matching speed, the rotation invariant edge points are selected from the rotation invariant pixels, and the selected points are used for rough matching to quickly screen out the unmatched areas. The theoretical analysis and experimental results show that the accuracy of this method is more than 95%. For the search map at any angle with the resolution at the level of 300,000 pixel, after selecting the appropriate pyramid series and threshold, the matching time can be controlled to within 0.1 s.

References

[1]	Y. Lee, T. Hara, H. Fujita, S. Itoh, T. Ishigaki, Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique, IEEE T. Med. Imaging, 20 (2001), 595–604. https://doi.org/10.1109/42.932744 doi: 10.1109/42.932744
[2]	H. Li, X. Zhang, S. Yao, B. Zhu, S. Fatikow, An improved template-matching-based pose tracking method for planar nanopositioning stages using enhanced correlation coefficient, IEEE Sens. J., 12 (2020), 6378–6387. https://doi.org/10.1109/JSEN.2020.2977370 doi: 10.1109/JSEN.2020.2977370
[3]	D. Pandey, U. Rawat, N. K. Rathore, K. Pandey, P. K. Shukla, Distributed biomedical scheme for controlled recovery of medical encrypted images, IRBM, 43 (2022), 151–160. https://doi.org/10.1016/j.irbm.2020.07.003 doi: 10.1016/j.irbm.2020.07.003
[4]	J. Gao, X. Li, J. Zhang, B. Lu, Image registration algorithm based on template matching, J. Xi'An Jiaotong Univ., 41 (2007), 307–311.
[5]	Z. Y. Liu, Y. M. Jiang, Occlusion workpiece recognition method based on template matching, Appl. Res. Comput., 37 (2020), 392–394.
[6]	T. Dekel, S. Oron, M. Rubinstein, S. Avidan, W. T. Freeman, Best-Buddies Similarity for robust template matching, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), 2021–2029. https://doi.org/10.1109/CVPR.2015.7298813
[7]	D. G. Lowe, Object recognition from local scale-invariant features, in Proceedings of the Seventh IEEE International Conference on Computer Vision, 2 (1999), 1150–1157. https://doi.org/10.1109/ICCV.1999.790410
[8]	H. Bay, A. Ess, L. V. Gool, Speeded-Up Robust Features (SURF), Comput. Vis. Image Understand., 110 (2008), 346–359. https://doi.org/10.1016/j.cviu.2007.09.014 doi: 10.1016/j.cviu.2007.09.014
[9]	M. J. Atallah, Faster image template matching in the sum of the absolute value of differences measure, IEEE T. Image Process., 10 (2001), 659–659. https://doi.org/10.1109/83.913600 doi: 10.1109/83.913600
[10]	K. Jia, S. R. Qu, Improved SSDA in image matching, Meas. Control, 10 (2012), 47–50.
[11]	J. S. Li, Y. Y. Tan, Y. L. Zhang, Improved algorithm of SSDA, Elec. Opt. Control, 14 (2007), 66–68.
[12]	E. Elboher, M. Werman, Asymmetric correlation: A noise robust similarity measure for template matching, IEEE T. Image Process., 22 (2013), 3062–3073. https://doi.org/10.1109/TIP.2013.2257811 doi: 10.1109/TIP.2013.2257811
[13]	M. F. Tombari, ZNCC-based template matching using bounded partial correlation, Pattern Recogn. Lett., 26 (2005), 2129–2134. https://doi.org/10.1016/j.patrec.2005.03.022 doi: 10.1016/j.patrec.2005.03.022
[14]	D. M. Tsai, C. T. Lin, Fast normalized cross correlation for defect detection, Pattern Recogn. Lett., 24 (2003), 2625–2631. https://doi.org/10.1016/S0167-8655(03)00106-5 doi: 10.1016/S0167-8655(03)00106-5
[15]	S. F. Yin, Y. C. Wang, L. C. Cao, G. F. Jin, Y. S. Ling, Fast correlation matching based on fast Fourier transform and integral graph, Acta Phtonica. Sin., 39 (2010), 2246–2250. https://doi.org/10.3788/gzxb20103912.2246 doi: 10.3788/gzxb20103912.2246
[16]	L. D. Stefano, S. Mattoccia, A sufficient condition based on the Cauchy-Schwarz inequality for efficient template matching, in Proceedings 2003 International Conference on Image Processing, 1 (2003), 269–272.
[17]	Y. M. Fouda, A. R. Khan, Normalize cross correlation algorithm in pattern matching based on 1-D information vector, Trends Appl. Sci. Res., 10 (2015), 195–206. https://doi.org/10.3923/tasr.2015.195.206 doi: 10.3923/tasr.2015.195.206
[18]	T. Cao, B. Li, F. J. Ren, R. Dong, Fast circular projection image matching algorithm, J. Int. Syst., 15 (2020), 84–91.
[19]	J. B. Zheng, L. X. Zheng, J. Q. Zhu, A fast template matching method based on gray scale, Mod. Comput., 626 (2018), 54–58.

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