Xunyang Liang, Qi YangThis email address is being protected from spambots. You need JavaScript enabled to view it., Yilu Wang, Shida Wang, and Xingzhuo Huang
School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China
Received: November 2, 2023 Accepted: November 24, 2023 Publication Date: January 27, 2024
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
In this paper, an adaptive fractional-order optical flow selection algorithm with improved ant colony clustering is proposed to address the issues of texture processing and dynamic noise perturbation in simultaneous localization and mapping algorithms in dynamic scenes with strong static assumption theory. The algorithm combines the characteristics of fractional differentiation and sparse optical flow algorithm, and makes full use of the weak texture gradient of the image. The ant colony algorithm is improved by using the elite sharing mechanism, and the improved ant colony algorithm is combined with the clustering algorithm. The experimental results show that the algorithm not only realizes the adaptive selection of the best order, but also achieves better dynamic disturbance differentiation ability through the clustering of feature selection. While distinguishing dynamic and static information effectively, more details of optical flow with weak gradient feature are preserved. The proposed algorithm holds promise for simultaneous localization and mapping systems.
[1] G. Johansson, (1973) “Visual perception of biological motion and a model for its analysis" Perception & psychophysics 14: 201–211.
[2] Q. Yang, D. Chen, T. Zhao, and Y. Chen, (2016) “Fractional calculus in image processing: a review" Fractional Calculus and Applied Analysis 19(5): 1222–1249.
[3] J. K. Suhr, (2009) “Kanade-lucas-tomasi (klt) feature tracker" Computer Vision (EEE6503): 9–18.
[4] S. Guha, R. Rastogi, and K. Shim, (1998) “CURE: An efficient clustering algorithm for large databases" ACM Sigmod record 27(2): 73–84.
[5] B. Z. Dadaneh, H. Y. Markid, and A. Zakerolhosseini, (2016) “Unsupervised probabilistic feature selection using ant colony optimization" Expert Systems with Applications 53: 27–42.
[6] X. Zhang, D. Boutat, and D. Liu. Applications of fractional operator in image processing and stability of control systems. 2023.
[7] Y. Di, J.-X. Zhang, and X. Zhang, (2023) “Alternate admissibility LMI criteria for descriptor fractional order systems with 0< α< 2" Fractal and Fractional 7(8): 577.
[8] H. Yan, J.-X. Zhang, and X. Zhang, (2022) “Injected Infrared and Visible Image Fusion via L_{1} Decomposition Model and Guided Filtering" IEEE Transactions on Computational Imaging 8: 162–173.
[9] W. Zhang, Y. Gao, S. Peng, D. Zhou, and B. Wang, (2022) “Fault diagnosis of hydroelectric units based on a novel multiscale fractional-order weighted permutation entropy" Fractal and Fractional 6(10): 588.
[10] X. Zhang, R. Liu, J. Ren, and Q. Gui, (2022) “Adaptive fractional image enhancement algorithm based on rough set and particle swarm optimization" Fractal and Fractional 6(2): 100.
[11] X. Zhang and L. Dai, (2022) “Image enhancement based on rough set and fractional order differentiator" Fractal and Fractional 6(4): 214.
[12] Y.-F. Pu, N. Zhang, Z.-N. Wang, J. Wang, Z. Yi, Y. Wang, and J.-L. Zhou, (2019) “Fractional-order Retinex for adaptive contrast enhancement of under-exposed traffic images" IEEE Intelligent Transportation Systems Magazine 13(1): 149–159.
[13] L. Deng, J. Qi, Y. Cheng, and J. Hu. “Medical Image Enhancement Base on Fractional Differential Approach”. In: 2020 Chinese Control And Decision Conference (CCDC). IEEE. 2020, 956–960.
[14] D. G. Lowe, (2004) “Distinctive image features from scale-invariant keypoints" International journal of computer vision 60: 91–110.
[15] E. Rosten and T. Drummond. “Fusing points and lines for high performance tracking”. In: Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1. 2. Ieee. 2005, 1508–1515.
[16] S. Leutenegger, M. Chli, and R. Y. Siegwart. “BRISK: Binary robust invariant scalable keypoints”. In: 2011 International conference on computer vision. Ieee. 2011, 2548–2555.
[17] H. Bay, T. Tuytelaars, and L. Van Gool. “Surf: Speeded up robust features”. In: Computer Vision– ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9. Springer. 2006, 404–417.
[18] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. “ORB: An efficient alternative to SIFT or SURF”. In: 2011 International conference on computer vision. Ieee. 2011, 2564–2571.
[19] J. Canny, (1986) “A computational approach to edge detection" IEEE Transactions on pattern analysis and machine intelligence (6): 679–698.
[20] S. Z. Li and G. Feng. Advances in biometric person authentication. Springer, 2004.
[21] M. Rostami, K. Berahmand, E. Nasiri, and S. Forouzandeh, (2021) “Review of swarm intelligencebased feature selection methods" Engineering Applications of Artificial Intelligence 100: 104210.
[22] R. Li and X. Zhang, (2019) “Adaptive sliding mode observer design for a class of T–S fuzzy descriptor fractional order systems" IEEE Transactions on Fuzzy Systems 28(9): 1951–1960.
[23] X. Zhang and J. Dong, (2020) “LMI criteria for admissibility and robust stabilization of singular fractional-order systems possessing poly-topic uncertainties" Fractal and Fractional 4(4): 58.
[24] X. Zhang and Y. Yan, (2020) “Admissibility of fractional order descriptor systems based on complex variables: An LMI approach" Fractal and Fractional 4(1): 8.
[25] D. Chen, Y. Chen, and D. Xue, (2015) “Fractional-order total variation image denoising based on proximity algorithm" Applied Mathematics and Computation 257: 537–545.
[26] X. Zhang and Y. Chen, (2018) “Admissibility and robust stabilization of continuous linear singular fractional order systems with the fractional order α: The 0< α< 1 case" ISA transactions 82: 42–50.
[27] D. Chen, H. Sheng, Y. Chen, and D. Xue, (2013) “Fractional-order variational optical flow model for motion estimation" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1990): 20120148.
[28] B. Chen, L. Chen, and Y. Chen, (2013) “Efficient ant colony optimization for image feature selection" Signal processing 93(6): 1566–1576.
[29] Y. Wu, J. Lim, and M.-H. Yang. “Online object tracking: A benchmark”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2013, 2411–2418.
[30] D. Y. Lee, H. Ko, J. Kim, and A. C. Bovik, (2021) “On the space-time statistics of motion pictures" JOSA A 38(7): 908–923.
[31] S. Sieranoja and P. Fränti, (2022) “Adapting k-means for graph clustering" Knowledge and Information Systems 64(1): 115–142.
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