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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Review Article

An Image Recognition Method Based on Dynamic System Synchronization

Author(s): Wanbo Yu, Xiaoran Chen* and Xiang Li

Volume 16, Issue 6, 2023

Published on: 26 December, 2022

Article ID: e011222211499 Pages: 7

DOI: 10.2174/2666255816666221201155914

Price: $65

Abstract

At present, image recognition technology first classifies images and outputs category information through the neural network. The next step involves the search. Before retrieval, the feature database needs to be established, followed by one-to-one correspondence. This method is tedious, time-consuming and has low accuracy. In computer vision research, researchers have proposed various image recognition methods to be applied in various fields and made many research achievements. However, at present, the accuracy, stability and time efficiency cannot meet the needs of practical work. In terms of UAV image recognition, high accuracy and low consumption are required. Previous methods require huge databases, which increases the consumption of UAVs. Taking aerial transmission and line images as the research object, this paper proposes a method of image recognition based on chaotic synchronization. Firstly, the image is used as a function to construct a dynamic system, and the function structure and parameters are adjusted to realize chaos synchronization. In this process, different types of images are identified. At the same time, we research this dynamic system characteristics and realize the mechanism of image recognition. Compared with other methods, the self-built aerial image data set for bird's nest identification, iron frame identification and insulator identification has the characteristics of a high identification rate and less calculation time. It is preliminarily proven that the method of synchronous image recognition is practical, and also worthy of further research, verification and analysis.

Graphical Abstract

[1]
N. Ma, Z. Wu, Y. Cheung, Y. Guo, Y. Gao, J. Li, and B. Jiang, "A survey of human action recognition and posture prediction", Tsinghua Sci. Technol., vol. 27, no. 6, pp. 973-1001, 2022.
[http://dx.doi.org/10.26599/TST.2021.9010068]
[2]
K. Wang, and M. Liu, "YOLO-Anti: YOLO-based counterattack model for unseen congested object detection", Pattern Recognit., vol. 131, p. 108814, 2022.
[http://dx.doi.org/10.1016/j.patcog.2022.108814]
[3]
P. Ren, Y. Xiao, and X. Chang, "A survey of deep active learning", ACM Comput. Surv., vol. 54, no. 9, pp. 1-40, 2022.
[4]
"A line-segment-based non-maximum suppression method for accurate object detection", Knowl. Base. Syst., vol. 251, no. 5, p. 108885, 2022.
[5]
W. Yu, and D. Wang, "Chaos characteristics of surface iteration and its application in face recognition", J. Comput. Aided Design Graphics, vol. 27, no. 12, p. 2264, 2015.
[6]
W.B. Yu, "Application of chaos in image processing and recognition", In 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC). Dec 25-27, 2017, Dalian, Chinapp. 1108-1113
[7]
W. Yu, X. Wang, and D. Wang, "Face image recognition based on discrete cosine transform basis function iteration", J. Graphics, vol. 41, no. 01, p. 88, 2020.
[8]
W. Yu, X. Li, and T. Yu, "Iterative recognition of bird’s nest in aerial photograph of high voltage transmission tower", Rec. Adv. Comput. Sci. Commun., vol. 15, no. 6, pp. 851-858, 2022.
[http://dx.doi.org/10.2174/2666255813999201022113313]
[9]
Y. Wang, and S. Hu, "Probabilistic latent semantic analysis for dynamic textures recognition and localization", J. Electron. Imaging, vol. 23, no. 6, p. 063006, 2014.
[http://dx.doi.org/10.1117/1.JEI.23.6.063006]
[10]
H.S. Wang, Study on chaotic attractors of images, PhD Thesis, Jilin University, 2015.
[11]
V. Venkataraman, and P. Turaga, "Shape distributions of nonlinear dynamical systems for video-based inference", IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 12, pp. 2531-2543, 2016.
[http://dx.doi.org/10.1109/TPAMI.2016.2533388] [PMID: 27824585]
[12]
I. Nejadgholi, "S.A. SeyyedSalehi and S. Chartier, “A brain-inspired method of facial expression generation using chaotic feature extracting bidirectional associative memory”", Neural Process. Lett., vol. 46, no. 3, pp. 943-960, 2017.
[http://dx.doi.org/10.1007/s11063-017-9615-5]
[13]
X. Lin, S. Zhou, H. Tang, Y. Qi, and X. Xie, "A novel fractional-order chaotic phase synchronization model for visual selection and shifting", Entropy, vol. 20, no. 4, p. 251, 2018.
[http://dx.doi.org/10.3390/e20040251] [PMID: 33265342]
[14]
Y. Jiang, H. Chen, X. Zhang, Y. Zhou, and L. Wang, "Combustion condition recognition of coal-fired kiln based on chaotic characteristics analysis of flame video", IEEE Trans. Industr. Inform., vol. 18, no. 6, pp. 3843-3852, 2022.
[http://dx.doi.org/10.1109/TII.2021.3118135]
[15]
B. Wang, F.C. Zou, and X.W. Liu, "New algorithm to generate the adversarial example of image", Optik, vol. 207, p. 164477, 2020.
[http://dx.doi.org/10.1016/j.ijleo.2020.164477]
[16]
J.B. Florindo, Multimedia Tools Appl., vol. 80, no. 19, pp. 29177-29197, 2021.
[http://dx.doi.org/10.1007/s11042-021-10959-0]
[17]
J. Rosen, H.B. De Aguiar, and V. Anand, "Roadmap on chaos-inspired imaging technologies (CI2-Tech)", Appl. Phy. B-Lasers Optics, vol. 128, no. 3, p. 49, .
[18]
Y. Luo, X. Yu, and D. Yang, "A new recognition algorithm for high‐voltage lines based on improved LSD and convolutional neural networks", IET Image Process., vol. 15, no. 1, pp. 260-268, 2021.
[http://dx.doi.org/10.1049/ipr2.12031]
[19]
XY Zheng, R Jia, and Aisikaer, "Component identification and defect detection in transmission lines based on deep learning", J. Intell. Fuzzy Syst., vol. 40, no. 2, pp. 3147-3158, 2021.
[20]
Y.Q. Zhang, "A hybrid convolutional neural network and relief-f algorithm for fault power line recognition in internet of things-based smart grids", Wirel. Commun. Mob. Comput., vol. 2022, p. 7, 2022.

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