Author Affiliations
1School of Resource & Environment Engineering, Wuhan University of Technology, Wuhan, Hubei 430079, China;2Chongqing Institute of Metrology and Quality Inspection, Chongqing 401120, Chinashow less
Fig. 1. Matching process of bidirectional consistency constraint
Fig. 2. Fusion process of slow in and out weighted fusion algorithm
Fig. 3. h(t) function curves under different conditions
Fig. 4. Power function weighted fusion process
Fig. 5. Power function weighting coefficient of Cell acceleration
Fig. 6. Matching results of image feature points under different luminance by different algorithms. (a) Traditional SURF algorithm; (b) proposed algorithm
Fig. 7. Matching results of image feature points under different angles by different algorithms. (a) Traditional SURF algorithm; (b) proposed algorithm
Fig. 8. Matching results of image feature points under different resolutions by different algorithms. (a) Traditional SURF algorithm; (b) proposed algorithm
Fig. 9. Matching results of image feature points under different scales by different algorithms. (a) Traditional SURF algorithm; (b) proposed algorithm
Fig. 10. Two original image sequences with different brightness. (a) Image 1; (b) image 2
Fig. 11. Comparison of results of three fusion algorithms on images for different brightness. (a) Slow in and slow out weighted fusion algorithm; (b) Ref. [16]; (c) proposed algorithm
Fig. 12. Two original image sequences with different angles. (a) Image 1; (b) image 2
Fig. 13. Comparison of results of three fusion algorithms on images from different angles. (a) Slow in and slow out weighted fusion algorithm; (b) Ref. [16]; (c) proposed algorithm
Fig. 14. Two original image sequences. (a)Image 1 (b)Image 2
Fig. 15. Comparison of results of three fusion algorithms on images with different resolutions. (a) Gradual fade weighted fusion algorithm; (b) Ref. [16]; (c) proposed algorithm
Fig. 16. Two original image sequences of different heights. (a) Image 1; (b) image 2
Fig. 17. Comparison of results of three fusion algorithms on images of different heights. (a) Slow in and slow out weighted fusion algorithm; (b) Ref. [16]; (c) proposed algorithm
Fig. 18. Time-consuming comparison histogram of 4 groups of experiments
Fig. 19. MSE comparison of related algorithms
Fig. 20. Information entropy data comparison
Test image | Rotate and zoom | Brightness | Blurry | Perspective |
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Optimal threshold | 0.97 | 0.96 | 0.98 | 0.99 |
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Table 1. Test results of K values
Algorithm | Different brightness | Different angles | Different resolutions | Different scales |
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Correct matching rate /% | Time /s | Correct matching rate /% | Time/s | Correctmatching rate /% | Time/s | Correctmatching rate /% | Time/s |
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Traditional SURF algorithm | 74.07 | 3.59 | 65.52 | 3.58 | 70.52 | 4.16 | 78.25 | 3.49 | Proposed algorithm | 85.18 | 2.01 | 76.80 | 2.02 | 82.10 | 2.10 | 90.15 | 2.00 |
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Table 2. Data comparison of related algorithms