The recognition of martial arts movements with the aid of computers has become crucial because of the vigorous promotion of martial arts education in schools in China to support the national essence and the inclusion of martial arts as a physical education test item in the secondary school examination in Shanghai. In this paper, the fundamentals of background difference algorithms are examined and a systematic analysis of the benefits and drawbacks of various background difference algorithms is presented. Background difference algorithm solutions are proposed for a number of common, challenging problems. The empty background is then automatically extracted using a symmetric disparity approach that is proposed for the initialization of background disparity in three-dimensional (3D) photos of martial arts action. It is possible to swiftly remove and manipulate the background, even in intricate martial arts action recognition scenarios. According to the experimental findings, the algorithm's optimized model significantly enhances the foreground segmentation effect of the backdrop disparity in 3D photos of martial arts action. The use of features such as texture probability is coupled to considerably enhance the shadow elimination effect for the shadow problem of background differences.
Citation: Chao Zhao, Bing Li, KaiYuan Guo. Adaptive enhancement design of non-significant regions of a Wushu action 3D image based on the symmetric difference algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14793-14810. doi: 10.3934/mbe.2023662
The recognition of martial arts movements with the aid of computers has become crucial because of the vigorous promotion of martial arts education in schools in China to support the national essence and the inclusion of martial arts as a physical education test item in the secondary school examination in Shanghai. In this paper, the fundamentals of background difference algorithms are examined and a systematic analysis of the benefits and drawbacks of various background difference algorithms is presented. Background difference algorithm solutions are proposed for a number of common, challenging problems. The empty background is then automatically extracted using a symmetric disparity approach that is proposed for the initialization of background disparity in three-dimensional (3D) photos of martial arts action. It is possible to swiftly remove and manipulate the background, even in intricate martial arts action recognition scenarios. According to the experimental findings, the algorithm's optimized model significantly enhances the foreground segmentation effect of the backdrop disparity in 3D photos of martial arts action. The use of features such as texture probability is coupled to considerably enhance the shadow elimination effect for the shadow problem of background differences.
| [1] |
A. Tulendiyeva, T. Saliev, Z. Andassova, A. Issabayev, I. Fakhradiyev, Historical overview of injury prevention in traditional martial arts, Sport Sci. Health, 17 (2021), 837–848. https://doi.org/10.1007/s11332-021-00785-0 doi: 10.1007/s11332-021-00785-0
|
| [2] |
H. Liang, Evaluation of fitness state of sports training based on self-organizing neural network, Neural Comput. Appl., 33 (2021), 3953–3965. https://doi.org/10.1007/s00521-020-05551-w doi: 10.1007/s00521-020-05551-w
|
| [3] |
S. Starke, Y. Zhao, F. Zinno, T. Komura, Neural animation layering for synthesizing martial arts movements, ACM Trans. Graphics, 40 (2021), 1–16. https://doi.org/10.1145/3450626.3459881 doi: 10.1145/3450626.3459881
|
| [4] |
M. Toshpulatov, W. Lee, S. Lee, A. H. Roudsari, Human pose, hand and mesh estimation using deep learning: a survey, J. Supercomput., 78 (2022), 7616–7654. https://doi.org/10.1007/s11227-021-04184-7 doi: 10.1007/s11227-021-04184-7
|
| [5] |
Z. J. Zha, J. Liu, T. Yang, Y. Zhang, Spatiotemporal-textual co-attention network for video question answering, ACM Trans. Multimedia Comput. Commun. Appl., 15 (2019), 1–18. https://doi.org/10.1145/3320061 doi: 10.1145/3320061
|
| [6] | H. Kwon, C. Tong, H. Haresamudram, Y. Gao, G. D. Abowd, N. D. Lane, et al., IMUTube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4 (2020), 1–29. https://doi.org/10.1145/3411841 |
| [7] |
D. A. Kumar, A. S. C. S. Sastry, P. V. V. Kishore, E. K. Kumar, Indian sign language recognition using graph matching on 3D motion captured signs, Multimedia Tools Appl., 77 (2018), 32063–32091. https://doi.org/10.1007/s11042-018-6199-7 doi: 10.1007/s11042-018-6199-7
|
| [8] |
L. H. Long, Role of artificial intelligence algorithm for taekwondo teaching effect evaluation model, J. Intell. Fuzzy Syst., 40 (2021), 3239–3250. https://doi.org/10.3233/JIFS-189364 doi: 10.3233/JIFS-189364
|
| [9] |
B. M. Craig, A. J. Lee, Stereotypes and structure in the interaction between facial emotional expression and sex characteristics, Adapt. Hum. Behav. Physiol., 6 (2020), 212–235. https://doi.org/10.1007/s40750-020-00141-5 doi: 10.1007/s40750-020-00141-5
|
| [10] |
Z. Wu, S. Shen, X. Lian, X. Su, E. Chen, A dummy-based user privacy protection approach for text information retrieval, Knowledge-Based Syst., 195 (2020), 105679. https://doi.org/10.1016/j.knosys.2020.105679 doi: 10.1016/j.knosys.2020.105679
|
| [11] |
R. Zhang, F. Torabi, G. Warnell, P. Stone, Recent advances in leveraging human guidance for sequential decision-making tasks, Auton. Agents Multi-Agent Syst., 35 (2021), 1–39. https://doi.org/10.1007/s10458-021-09514-w doi: 10.1007/s10458-021-09514-w
|
| [12] |
Z. Wu, S. Xuan, J. Xie, C. Lin, C. Lu, How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective, Comput. Biol. Med., 147 (2022), 105726. https://doi.org/10.1016/j.compbiomed.2022.105726 doi: 10.1016/j.compbiomed.2022.105726
|
| [13] |
A. K. Mackenzie, M. L. Vernon, P. R. Cox, D. Crundall, R. C. Daly, D. Guest, et al., The multiple object avoidance (MOA) task measures attention for action: Evidence from driving and sport, Behav. Res. Methods, 54 (2022), 1508–1529. https://doi.org/10.3758/s13428-021-01679-2 doi: 10.3758/s13428-021-01679-2
|
| [14] |
Z. Wu, S. Shen, H. Li, H. Zhou, C. Lu, A basic framework for privacy protection in personalized information retrieval: An effective framework for user privacy protection, J. Organ. End User Comput., 33 (2021), 1–26. https://doi.org/10.4018/JOEUC.292526 doi: 10.4018/JOEUC.292526
|
| [15] |
M. Rana, V. Mittal, Wearable sensors for real-time kinematics analysis in sports: a review, IEEE Sens. J., 21 (2020), 1187–1207. https://doi.org/10.1109/JSEN.2020.3019016 doi: 10.1109/JSEN.2020.3019016
|
| [16] |
Z. Wu, G. Li, S. Shen, X. Lian, E. Chen, G. Xu, Constructing dummy query sequences to protect location privacy and query privacy in location-based services, World Wide Web, 24 (2021), 25–49. https://doi.org/10.1007/s11280-020-00830-x doi: 10.1007/s11280-020-00830-x
|
| [17] |
F. Malawski, Depth versus inertial sensors in real-time sports analysis: a case study on fencing, IEEE Sens. J., 21 (2020), 5133–5142. https://doi.org/10.1109/JSEN.2020.3036436 doi: 10.1109/JSEN.2020.3036436
|
| [18] |
Z. Wu, S. Shen, H. Zhou, H. Li, C. Lu, D. Zou, An effective approach for the protection of user commodity viewing privacy in e-commerce website, Knowledge-Based Syst., 220 (2021), 106952. https://doi.org/10.1016/j.knosys.2021.106952 doi: 10.1016/j.knosys.2021.106952
|
| [19] |
E. Kon, B. D. Matteo, P. Verdonk, M. Drobnic, O. Dulic, G. Gavrilovic, et al., Aragonite-based Scaffold for the treatment of joint surface lesions in mild to moderate osteoarthritic knees: results of a 2-year multicenter prospective study, Am. J. Sports Med., 49 (2021), 588–598. https://doi.org/10.1177/0363546520981750 doi: 10.1177/0363546520981750
|
| [20] |
Z. Liu, L. Li, S. Liu, Y. Sun, S. Li, M. Yi, et al., Reduced feelings of regret and enhanced fronto-striatal connectivity in elders with long-term Tai Chi experience, Social Cognit. Affective Neurosci., 15 (2020), 861–873. https://doi.org/10.1093/scan/nsaa111 doi: 10.1093/scan/nsaa111
|
| [21] |
K. Petri, P. Emmermacher, M. Danneberg, S. Masik, F. Eckardt, S. Weichelt, et al., Training using virtual reality improves response behavior in karate kumite, Sports Eng., 22 (2019), 1–12. https://doi.org/10.1007/s12283-019-0299-0 doi: 10.1007/s12283-019-0299-0
|
| [22] |
R. Lozada-Yánez, N. La-Serna-Palomino, F. Molina-Granj, Augmented reality and MS-kinect in the learning of basic mathematics: KARMLS case, Int. Educ. Stud., 12 (2019), 54–69. https://doi.org/10.5539/ies.v12n9p54 doi: 10.5539/ies.v12n9p54
|
| [23] |
J. C. Zhou, J. M. Sun, W. S. Zhang, Z. F. Lin, Multi-view underwater image enhancement method via embedded fusion mechanism, Eng. Appl. Artif. Intell., 121 (2023), 105946. https://doi.org/10.1016/j.engappai.2023.105946 doi: 10.1016/j.engappai.2023.105946
|
| [24] |
P. Parrend, P. Collet, A review on complex system engineering, J. Syst. Sci. Complexity, 33 (2020), 1755–1784. https://doi.org/10.1007/s11424-020-8275-0 doi: 10.1007/s11424-020-8275-0
|
| [25] |
X. Huang, R. Ball, W. Wang, Comparative study of industrial design undergraduate education in China and USA, Int. J. Technol. Des. Educ., 31 (2021), 565–586. https://doi.org/10.1007/s10798-020-09563-4 doi: 10.1007/s10798-020-09563-4
|
| [26] |
H. W. Wu, E. Fajiculay, J. F. Wu, C. C. S. Yan, C. P. Hsu, S. H. Wu, Noise reduction by upstream open reading frames, Nat. Plants, 8 (2020), 474–480. https://doi.org/10.1038/s41477-022-01136-8 doi: 10.1038/s41477-022-01136-8
|
| [27] |
E. O. Abiodun, A. Alabdulatif, O. I. Abiodun, M. Alawida, A. Alabdulatif, R. S. Alkhawaldeh, A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities, Neural Comput. Appl., 33 (2021), 15091–15118. https://doi.org/10.1007/s00521-021-06406-8 doi: 10.1007/s00521-021-06406-8
|
| [28] |
H. Dong, L. Zhao, Y. Shu, N. N. Xiong, X-ray image denoising based on wavelet transform and median filter, Appl. Math. Nonlinear Sci., 5 (2020), 435–442. https://doi.org/10.2478/amns.2020.2.00062 doi: 10.2478/amns.2020.2.00062
|
| [29] |
L. Jiang, T. Zhang, Y. Feng, Identifying the critical factors of sustainable manufacturing using the fuzzy DEMATEL method, Appl. Math. Nonlinear Sci., 5 (2020), 391–404. https://doi.org/10.2478/amns.2020.2.00045 doi: 10.2478/amns.2020.2.00045
|
| [30] |
J. Feng, M. Meng, S. Liu, X. Zhang, J. Yuan, Z. Zhang, Prediction of Chinese automobile growing trend considering vehicle adaptability based on Cui–Lawson model, Appl. Math. Nonlinear Sci., 5 (2020), 367–376. https://doi.org/10.2478/amns.2020.2.00054 doi: 10.2478/amns.2020.2.00054
|
| [31] |
Z. Lao, D. Pan, H. Yuan, J. Ni, S. Ji, W. Zhu, et al., Mechanical-tunable capillary-force-driven self-assembled hierarchical structures on soft substrate, ACS Nano, 12 (2018), 10142–10150. https://doi.org/10.1021/acsnano.8b05024 doi: 10.1021/acsnano.8b05024
|
| [32] |
X. Luo, C. Zhang, L. Bai, A fixed clustering protocol based on random relay strategy for EHWSN, Digital Commun. Networks, 9 (2023), 90–100. https://doi.org/10.1016/j.dcan.2022.09.005 doi: 10.1016/j.dcan.2022.09.005
|
| [33] |
J. C. Zhou, L. Pang, W. S. Zhang, Underwater image enhancement method by multi-interval histogram equalization, IEEE J. Oceanic Eng., 48 (2023), 474–488. https://doi.org/10.1109/JOE.2022.3223733 doi: 10.1109/JOE.2022.3223733
|
| [34] |
Z. K. Wang, H. L. Zhen, J. D. Deng, Q. F Zhang, X. J. Li, M. X. Yuan, et al., Multiobjective optimization-aided decision-making system for large-scale manufacturing planning, IEEE Trans. Cybern., 52 (2022), 8326–8339. https://doi.org/10.1109/TCYB.2021.3049712 doi: 10.1109/TCYB.2021.3049712
|
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Chao Zhao, Bing Li, KaiYuan Guo. Adaptive enhancement design of non-significant regions of a Wushu action 3D image based on the symmetric difference algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14793-14810. doi: 10.3934/mbe.2023662
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