Quality Evaluation of Multi-Frame Sports Image Fusion based on Gradient Domain

doi:10.23940/ijpe.20.03.p10.411418

Abstract

Abstract: In order to improve the recognition ability of the detail action of a multi-frame sports image, a local fusion quality evaluation method based on gradient domain is proposed. The sampling model of visual feature information of multi-frame sports image is established, the gradient feature decomposition and action feature reconstruction of multi-frame sports image are carried out by using block feature fusion matching method, and the key motion feature point matching model of multi-frame sports image is established. The local fuzzy feature detection of multi-frame sports images is carried out by template automatic matching and gradient domain feature decomposition. The block identification model of local fuzzy features of multi-frame sports images is established. The adaptive image information enhancement technology is used for multi-scale feature decomposition and information enhancement processing. The feature set of the key action of the local fuzzy region of the multi-frame sports image is extracted. According to the feature distribution of the block area of the key action, the fusion quality evaluation of the multi-frame sports image is realized. The simulation results show that the method has high accuracy in evaluating the fusion quality of multi-frame sports images, has a strong ability to distinguish multi-frame sports movements, and has guiding value in the guidance and judgment of multi-frame sports training.

Key words: gradient domain, multi-frame sports image, fusion quality, evaluation

Lili Zhang and Shuai Liu. Quality Evaluation of Multi-Frame Sports Image Fusion based on Gradient Domain [J]. Int J Performability Eng, 2020, 16(3): 411-418.

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References

1. Yang L.Multi-focus image fusion method based on NSST and ⅡCM[C]. Proceedings of the 2017 International Conference on Emerging Internetworking, Data and Web Technologies. Berlin: Springer, 2017:679-689.
2. Yang L.Technique for image de-noising based on non-subsampled shearlet transform and improved intuitionistic fuzzy entropy[J]. Optik-International Journal for Light and Electron Optics, 2015, 126(4):446-453.
3. Shen X X, Zhang H, Gao Z.Behavior recognition algorithm based on Kinect and pyramid feature[J]. Journal of Optoelectronics·Laser, 2014(2):357-363.
4. Yan S, Xu D, Zhang B, et al.Graph embedding and extensions: a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1):40-51.
5. Lin Y, Li Y, Yin X, et al.Multisensor Fault Diagnosis Modeling Based on the Evidence Theory[J]. IEEE Transactions on Reliability, 2018, 67(02): 513-521.
6. Hou C, Nie F, Li X, et al.Joint embedding learning and sparse regression: a framework for unsupervised feature selection[J]. IEEE Transactions on Cybernetics, 2014, 44(6): 793-804.
7. Shang Fangxin, Guo Hao, Li Gang, Zhang Ling.Novel image segmentation method with noise based on One-class SVM. Journal of Computer Applications, 2019, 39(3): 874-881.
8. Yun Lin,Chao Wang,Jiaxing Wang,Zheng Dou.A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks[J]. Sensors, 2016, 16(10): 1-22.
9. Azizpour H, Razavian A S, Sullivan J.From generic to specific deep representations for visual recognition[C]//Proceedings of the 2015 International Conference on Computer Vision and Pattern Recognition Workshops. Washington, DC: IEEE Computer Society,2015:36-45.
10. Razavian A S, Sullivan J, Carlsson S.Visual instance retrieval with deep convolutional networks[J]. ITE Transactions on Media Technology and Applications, 2016, 4(3):251-258.
11. Hassner T, Harel S, Paz E, et al.Effective face frontalization in unconstrained images[C]//Proceedings of the 2015 IEEE Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2015:4295-4304.
12. Liu N, Han J.DHSNet: deep hierarchical saliency network for salient object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2016:678-686.
13. Kim W, Kim C.Spatiotemporal saliency detection using textural contrast and its applications[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2014, 24(4):646-659.
14. Yun Lin, Meiyu Wang, Xianglong Zhou, Guoru Ding,Shiwen Mao.Dynamic Spectrum Interaction of UAV Flight Formation Communication with Priority: A Deep Reinforcement Learning Approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2020. DOI: 10.1109/TCCN.2020.2973376.
15. Wang Xiaoyu, Zhang Yazhou, Chen Deyun.Face detection based on MB-LBP and eye tracking[J]. Chinese Journal of Scientific Instrument,2014, 35(12):2739-2745.

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