Abstract
In order to solve the problems such as time consuming and mismatching in the experiment of eliminating SIFT mismatch points in RANSAC algorithm, proposed Mismatching Elimination Algorithm in SIFT Based on Function Fitting; Firstly, we use SIFT algorithm to direct the matching of the image and the matching image, using iterative least squares fitting method to construct function model for the key points of matched Image; secondly, fit the function model with the key points of matching image features; Finally, the errors of the two algorithms are calculated, when the error is greater than the set threshold, verify that the point is a mismatch point, and it is eliminated. The experimental results show that using Mismatching Elimination Algorithm in SIFT Based on Function Fitting than RANSAC algorithm in time to save the 2 s on average, the correct matching rate is increased by 11.75%, and more correct matching points can be reserved.
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References
Liu, X., Lei, Z.: Multi-modal image matching based on local frequency information. EURASIP J. Adv. Signal Process. 3(1), 1–11 (2013)
Ren, G., Peng, D., Gu, Y.: Fast image stitching algorithm based on cylindrical surface mapping. Appl. Res. Comput. 34(11), 1–8 (2017)
Li, G., Chen, Z.: Research status and prospect of visual tracking technology. Appl. Res. Comput. 27(8), 2814–2821 (2017)
Tan, S., Liu, Y., Li, Y.: Kernel correlation filtering target tracking algorithm based on Gauss scale space. Comput. Eng. Appl. 53(1), 29–33, +141 (2017)
Liu, L., Sun, K., Xu, H.: A fast matching algorithm for large scale images based on Hash characteristics. Comput. Eng. Appl. 53(17), 202–206, +211 (2017)
Wang, Q., Wang, B.: Local matching algorithm for image shopping search. Comput. Eng. Appl. 53(6), 246–251 (2017)
Wu, X., He, Y., Yang, L.: Two valued image retrieval based on improved shape context feature. Opt. Precis. Eng. 23(1), 302–309 (2015)
Yong, C., Lei, S.: Improved SIFT image registration algorithm on characteristic statistical distributions and consistency constraint. Opt.-Int. J. Light. Electron Opt. 127(2), 900–911 (2016)
Zhang, J., Zhang, H., Luo, Y.: An improved image registration method based on Harris corner detection. Laser Infrared 47(2), 230–233 (2017)
Chen, Y., Sun, Q., Xu, H.: Remote sensing image matching method based on SURF algorithm and RANSAC algorithm. Comput. Sci. Explor. 6(9), 822–828 (2012)
Yu, B., Guo, L., Zhao, T.: An adaptive hybridz bilateral filtering algorithm for infrared images. Infrared Laser Eng. 41(11), 3102–3107 (2012)
Di, N., Li, G., Wei, Y.: Terminal guidance chart using SIFT image matching technology. Infrared Laser Eng. 40(8), 1589–1593 (2011)
Yan, Y.: Cognitive fusion of thermal and visible imagery for effective detection and tracking of pedestrians in videos. Cogn. Comput. 10(1), 94–104 (2018)
Cheng, D., Li, Y., Yu, R.: Image matching method based on improved SIFT algorithm. Comput. Simul. 28(7), 285–289 (2011)
Hou, X.: The Research of Image Matching Technology Based on Local Feature Detection. Xidian University, Xi’an (2014)
Tian, J.: Cylindrical image matching algorithm based on curve fitting. Electron Meas. Technol. 39(2), 61–63, +68 (2016)
Wang, Z.: A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing 287, 68–83 (2018)
Han, J.: Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans. Geosci. Remote Sens. 53(6), 3325–3337 (2015)
Ren, J.: Real-time modeling of 3-D soccer ball trajectories from multiple fixed cameras. IEEE Trans. Circuits Syst. Video Technol. 18(3), 350–362 (2008)
Zhou, Y.: Hierarchical visual perception and two-dimensional compressive sensing for effective content-based color image retrieval. Cogn. Comput. 8(5), 877–889 (2016)
Yan, Y.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern Recogn. 79, 65–78 (2018)
Yan, Y.: Adaptive fusion of color and spatial features for noise-robust retrieval of colored logo and trademark images. Multidimens. Syst. Signal Process. 27(4), 945–968 (2016)
Chai, Y.: Hierarchical and multi-featured fusion for effective gait recognition under variable scenarios. Pattern Anal. Appl. 19(4), 905–917 (2016)
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Zhong, X., Li, Y., Ren, J. (2018). Mismatching Elimination Algorithm in SIFT Based on Function Fitting. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_23
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DOI: https://doi.org/10.1007/978-3-030-00563-4_23
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