American Institute of Mathematical Sciences

Advanced
August  2020, 3(3): 141-155. doi: 10.3934/mfc.2020021

A fast matching algorithm for the images with large scale disparity

Shichu Chen 1,, , Zhiqiang Wang 2, and  Yan Ren 2,

1. 

Computer Science Department, Curtin University, Perth, Australia
2. 

Automation College, Shenyang Aerospace University, Liaoning, China

* Corresponding author: Shichu Chen

Received  October 2019 Revised  March 2020 Published  June 2020

With the expansion of application areas of unmanned aerial vehicle (UAV) applications, there is a rising demand to realize UAV navigation by means of computer vision. Speeded-Up Robust Features (SURF) is an ideal image matching algorithm to be applied to solve the location for UAV. However, if there is a large scale difference between two images with the same scene taken by UAV and satellite respectively, it is difficult to apply SURF to complete the accurate image matching directly. In this paper, a fast image matching algorithm which can bridge the huge scale gap is proposed. The fast matching algorithm searches an optimal scaling ratio based on the ground distance represented by pixel. Meanwhile, a validity index for validating the performance of matching is given. The experimental results illustrate that the proposed algorithm performs better performance both on speed and accuracy. What's more, the proposed algorithm can also obtain the correct matching results on the images with rotation. Therefore, the proposed algorithm could be applied to location and navigation for UAV in future.

Keywords: UAV, SURF, matching, location, scale.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.
Citation: Shichu Chen, Zhiqiang Wang, Yan Ren. A fast matching algorithm for the images with large scale disparity. Mathematical Foundations of Computing, 2020, 3 (3) : 141-155. doi: 10.3934/mfc.2020021
References:
[1]

W. An, R. Yu and Y. Wu, An image registration algorithm based on FAST, RANSAC and SURF, Information Technology and Applications, (2015), 329–333. Google Scholar

[2]

T. C. Ao, X. D. Liu, Y. Ren, R. Luo and J. Xi, An approach to scene matching algorithm for UAV autonomous navigation, Chinese Control and Decision Conference, (2018), 996–1001. doi: 10.1109/CCDC.2018.8407275.  Google Scholar

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R. Dong, M. Liu and F. Li, Multiplayer convolutional feature aggregation algorithm for image retrieval, Mathematical Problems in Engineering, 2019 (2019), 1-12.   Google Scholar

[6]

A. S. Eltanany, M. Safy and A. S. Amein, Key point detection techniques, Advances in Intelligent Systems and Computing, 1058 (2019). doi: 10.1007/978-3-030-31129-2_82.  Google Scholar

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H. Jan, B. Rodrigo and S. Bernt, Learning Non-maximum Suppression, Conference on Computer Vision and Pattern Recognition, (2017), 6469–6477. Google Scholar

[8]

S. Katta, S. Pabboju, A. V. Babu and R. Akhila, CBIR using SIFT with LoG, DoG and PCA, Data Engineering and Communication Technology, (2020), 623–637. Google Scholar

[9]

X. Li, J. Luo, C. Duan and P. Yin, Portrait matching based on sift features, IOP Conference Series: Materials Science and Engineering, 612 (2019), 032162. doi: 10.1088/1757-899X/612/3/032162.  Google Scholar

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X. Li, X. Wang and C. Cheng, Application of scene recognition technology based on faster and surf algorithm in augmented reality, International Conference on Smart and Sustainable City, (2017), 23. Google Scholar

[11]

L. Liberti and C. Lavor, Euclidean Distance Geometry: An introduction, Springer Undergraduate Texts in Mathematics and Technology, Springer, Cham, 2017. doi: 10.1007/978-3-319-60792-4.  Google Scholar

[12]

X. Liu, X. Tao and N. Ge, Fast remote-sensing image registration using priori information and robust feature extraction, Tsinghua Science and Technology, 21 (2016), 552-560.   Google Scholar

[13]

Y. L. Liu, H. Zhang, H. L. Guo and N. N. Xiong, A FAST-BRISK feature detector with depth information, Sensors, 18 (2018), 3908. doi: 10.3390/s18113908.  Google Scholar

[14]

L. Tong and X. Ying, 3D point cloud initial registration using surface curvature and SURF matching, 3D Research, 9 (2018). doi: 10.1007/s13319-018-0193-8.  Google Scholar

[15]

C. Tsai and Y. Lin, An accelerated image matching technique for UAV orthoimage registration, ISPRS Journal of Photogrammetry and Remote Sensing, 128 (2017), 130-145.  doi: 10.1016/j.isprsjprs.2017.03.017.  Google Scholar

[16]

C. Y. Wang, Z. Zhang, Q. W. Li and X. Zhou, An image copy-move forgery detection method based on SURF and PCET, Institute of Electrical and Electronics Engineers, 7 (2019), 170032-170047.  doi: 10.1109/ACCESS.2019.2955308.  Google Scholar

[17]

D. Xiang, B. Zhong and K. Ma, A location-aware scale-space method for salient object detection, International Conference on Image Processing, (2015), 4195–4199. doi: 10.1109/ICIP.2015.7351596.  Google Scholar

[18]

K. Yang, A. N. Pan, Y. Yang, S. Zhang, S. H. Ong and H. L. Tang, Remote sensing image registration using multiple image features, Remote Sensing, 9 (2017), 581. doi: 10.3390/rs9060581.  Google Scholar

[19]

D. Zeng, L. D. Wu, B. Y. Chen and W. Shen, Slope-restricted multi-scale feature matching for geostationary satellite remote sensing images, Remote Sensing, 9 (2017), 576. doi: 10.3390/rs9060576.  Google Scholar

[20]

L. Zhu, Y. Wang, B. Zhao and X. Z. Zhang, A fast image stitching algorithm based on improved SURF, International Conference on Computational Intelligence and Security, (2015), 171–175. doi: 10.1109/CIS.2014.67.  Google Scholar

show all references

References:
[1]

W. An, R. Yu and Y. Wu, An image registration algorithm based on FAST, RANSAC and SURF, Information Technology and Applications, (2015), 329–333. Google Scholar

[2]

T. C. Ao, X. D. Liu, Y. Ren, R. Luo and J. Xi, An approach to scene matching algorithm for UAV autonomous navigation, Chinese Control and Decision Conference, (2018), 996–1001. doi: 10.1109/CCDC.2018.8407275.  Google Scholar

[3]

F. Bulbul, F. Badsha and R. Islam, Object detection by point feature matching using Matlab, Advances in Image and Video Processing, 6 (2018). doi: 10.14738/aivp.66.5619.  Google Scholar

[4]

M. B. Cohen, B. T. Fasy, G. L. Miller, A. Nayyeri, D. R. Sheehy and A. Velingker, Approximating nearest neighbor distances, Algorithms and Data Structures, Lecture Notes in Comput. Sci., Springer, Cham, 9214 (2015), 200-211.  doi: 10.1007/978-3-319-21840-3_17.  Google Scholar

[5]

R. Dong, M. Liu and F. Li, Multiplayer convolutional feature aggregation algorithm for image retrieval, Mathematical Problems in Engineering, 2019 (2019), 1-12.   Google Scholar

[6]

A. S. Eltanany, M. Safy and A. S. Amein, Key point detection techniques, Advances in Intelligent Systems and Computing, 1058 (2019). doi: 10.1007/978-3-030-31129-2_82.  Google Scholar

[7]

H. Jan, B. Rodrigo and S. Bernt, Learning Non-maximum Suppression, Conference on Computer Vision and Pattern Recognition, (2017), 6469–6477. Google Scholar

[8]

S. Katta, S. Pabboju, A. V. Babu and R. Akhila, CBIR using SIFT with LoG, DoG and PCA, Data Engineering and Communication Technology, (2020), 623–637. Google Scholar

[9]

X. Li, J. Luo, C. Duan and P. Yin, Portrait matching based on sift features, IOP Conference Series: Materials Science and Engineering, 612 (2019), 032162. doi: 10.1088/1757-899X/612/3/032162.  Google Scholar

[10]

X. Li, X. Wang and C. Cheng, Application of scene recognition technology based on faster and surf algorithm in augmented reality, International Conference on Smart and Sustainable City, (2017), 23. Google Scholar

[11]

L. Liberti and C. Lavor, Euclidean Distance Geometry: An introduction, Springer Undergraduate Texts in Mathematics and Technology, Springer, Cham, 2017. doi: 10.1007/978-3-319-60792-4.  Google Scholar

[12]

X. Liu, X. Tao and N. Ge, Fast remote-sensing image registration using priori information and robust feature extraction, Tsinghua Science and Technology, 21 (2016), 552-560.   Google Scholar

[13]

Y. L. Liu, H. Zhang, H. L. Guo and N. N. Xiong, A FAST-BRISK feature detector with depth information, Sensors, 18 (2018), 3908. doi: 10.3390/s18113908.  Google Scholar

[14]

L. Tong and X. Ying, 3D point cloud initial registration using surface curvature and SURF matching, 3D Research, 9 (2018). doi: 10.1007/s13319-018-0193-8.  Google Scholar

[15]

C. Tsai and Y. Lin, An accelerated image matching technique for UAV orthoimage registration, ISPRS Journal of Photogrammetry and Remote Sensing, 128 (2017), 130-145.  doi: 10.1016/j.isprsjprs.2017.03.017.  Google Scholar

[16]

C. Y. Wang, Z. Zhang, Q. W. Li and X. Zhou, An image copy-move forgery detection method based on SURF and PCET, Institute of Electrical and Electronics Engineers, 7 (2019), 170032-170047.  doi: 10.1109/ACCESS.2019.2955308.  Google Scholar

[17]

D. Xiang, B. Zhong and K. Ma, A location-aware scale-space method for salient object detection, International Conference on Image Processing, (2015), 4195–4199. doi: 10.1109/ICIP.2015.7351596.  Google Scholar

[18]

K. Yang, A. N. Pan, Y. Yang, S. Zhang, S. H. Ong and H. L. Tang, Remote sensing image registration using multiple image features, Remote Sensing, 9 (2017), 581. doi: 10.3390/rs9060581.  Google Scholar

[19]

D. Zeng, L. D. Wu, B. Y. Chen and W. Shen, Slope-restricted multi-scale feature matching for geostationary satellite remote sensing images, Remote Sensing, 9 (2017), 576. doi: 10.3390/rs9060576.  Google Scholar

[20]

L. Zhu, Y. Wang, B. Zhao and X. Z. Zhang, A fast image stitching algorithm based on improved SURF, International Conference on Computational Intelligence and Security, (2015), 171–175. doi: 10.1109/CIS.2014.67.  Google Scholar

Figure 1.  A region of intensities can be calculated in three additions on integral image
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Figure 2.  Left to right: templates of Gaussian second order partial derivative $ L_{yy} $ and $ L_{xy} $ separately; Approximations of corresponding box filters $ D_{yy} $ and $ D_{xy} $ respectively
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Figure 3.  Filters $ D_{yy} $ (above) and $ D_{xy} $ (below) with two size: $ 9\times 9 $ templates (left) and $ 15\times 15 $ templates (right)
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Figure 4.  Filters and octaves permutation
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Figure 5.  Scale space
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Figure 6.  $ 3\times 3 \times 3 $ neighbourhood non-maximum suppression
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Figure 7.  Haar wavelet templates in $ x $ and $ y $ directions
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Figure 8.  A sliding sector to find out dominant orientation
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Figure 9.  Descriptor and sub-region divisions
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Figure 10.  Different local characteristics
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Figure 11.  Flowchart of fast matching algorithm
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Figure 12.  (b) and (d) is the UAV image A; (a) and (c) are the matched tiles with Image A via Ao' method and the proposed method respectively
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Figure 13.  (b) and (d) is the UAV image B; (a) and (c) are the matched tiles with Image B via Ao' method and the proposed method respectively
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Figure 14.  (b) and (d) is the UAV image C; (a) and (c) are the matched tiles with Image A via Ao' method and the proposed method respectively
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Figure 15.  (b) and (d) is the UAV image D; (a) and (c) are the matched tiles with Image A via Ao' method and the proposed method respectively
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Figure 16.  Diagram of the angle between $ \overline{AB} $ and Google map direction
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Figure 17.  The matching results for Image B with rotations
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Figure 18.  The matching results for Image C with rotations
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Table 1.  Pseudo-code of fast matching algorithm
Algorithm: Fast matching algorithm for images with large scale disparity
Input: UAV aerial image $ I_{UAV} $, satellite tiles $ Tile_{i} $, $ i=1 $, 2, ..., n and $ C = 2 $.
Output: Best matching tile $ Tile_{b} $ with $ I_{scaled} $.
1: $ \alpha_{best} = \frac{D_{UAV}}{D_{Tile}} \times C $;
2: Reduce $ I_{UAV} $ with $ \alpha_{best} $ to get $ I_{scaled} $;
3: Let $ Value_{i} $ represent the corresponding matching performance between $ I_{scaled} $ and $ Tile_{b} $;
4: for $ i:=1 $ to $ n $ do
5: Double the size of $ Tile_{i} $;
6: Do the matching between the doubled $ Tile_{i} $ and $ I_{scaled} $;
7: Matching performance is valued by $ Value_{i} $
8: end for
9: $ b=argmax_{i} {Value_{i}} $ and $ Tile_{b} $ is the best matching tile with $ I_{scaled} $.
Stop.
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Algorithm: Fast matching algorithm for images with large scale disparity
Input: UAV aerial image $ I_{UAV} $, satellite tiles $ Tile_{i} $, $ i=1 $, 2, ..., n and $ C = 2 $.
Output: Best matching tile $ Tile_{b} $ with $ I_{scaled} $.
1: $ \alpha_{best} = \frac{D_{UAV}}{D_{Tile}} \times C $;
2: Reduce $ I_{UAV} $ with $ \alpha_{best} $ to get $ I_{scaled} $;
3: Let $ Value_{i} $ represent the corresponding matching performance between $ I_{scaled} $ and $ Tile_{b} $;
4: for $ i:=1 $ to $ n $ do
5: Double the size of $ Tile_{i} $;
6: Do the matching between the doubled $ Tile_{i} $ and $ I_{scaled} $;
7: Matching performance is valued by $ Value_{i} $
8: end for
9: $ b=argmax_{i} {Value_{i}} $ and $ Tile_{b} $ is the best matching tile with $ I_{scaled} $.
Stop.
Table 2.  Comparisons on time-consuming and numbers of matched pairs
Image No. Matching time (second) Numbers of matched pairs
Image A using fast method 23.1 7
Image A using Ao's method 738.1 3
Image B using fast method 23.0 6
Image B using Ao's method 703.7 3
Image C using fast method 24.1 6
Image C using Ao's method 749.9 7
Image D using fast method 23.3 7
Image D using Ao's method 697.5 9
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Image No. Matching time (second) Numbers of matched pairs
Image A using fast method 23.1 7
Image A using Ao's method 738.1 3
Image B using fast method 23.0 6
Image B using Ao's method 703.7 3
Image C using fast method 24.1 6
Image C using Ao's method 749.9 7
Image D using fast method 23.3 7
Image D using Ao's method 697.5 9
Table 3.  Comparisons of real scene direction with calculated scene rotation direction
Image No. Real image direction (degree) Calculated image direction (degree)
15 16.44
30 29.57
Image B 45 49.10
60 59.82
75 76.25
90 93.21
110 109.07
120 117.15
Image C 130 130.09
140 138.96
150 149.41
160 159.54
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Image No. Real image direction (degree) Calculated image direction (degree)
15 16.44
30 29.57
Image B 45 49.10
60 59.82
75 76.25
90 93.21
110 109.07
120 117.15
Image C 130 130.09
140 138.96
150 149.41
160 159.54
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