Mobile Robot Vision Image Feature Recognition Method Based on Machine Vision




SIFT feature point classification, Fourier series, Harris algorithm, Visual image


In order to improve the efficiency and accuracy of mobile robot visual image feature recognition, a mobile robot visual image feature recognition method based on machine vision is proposed in this paper. Firstly, the development of mobile robot vision is analyzed, and the specific functions of robot visual feature recognition method are designed; Then, the Fourier series method is used to collect the mobile robot visual image, and the matrix associated with the autocorrelation function is calculated according to the Harris algorithm to complete the edge feature extraction of the mobile robot visual image; SIFT feature points of mobile robot visual image are classified, and mobile robot visual image feature recognition is realized through machine vision. The experimental results show that when the number of images is 600, the accuracy of image feature recognition and the loss value of image edge feature extraction of this method are 96.98% and 6.38%, respectively, and the number of iterations is 500. The time of visual image feature recognition of this method is only 3 minutes; The method has the lowest error mean and error variance under different noise conditions. This method can effectively improve the efficiency and accuracy of image feature recognition.


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J.C. Huang, Q. Shu, X.C. Zhu, L. Zhou, H.Z. Liu, J. Lin, “Robot Visual Recognition and Sorting Strategy Based on Transfer Learning”, Computer engineering and Application, Vol.55, No.08, pp. 232-237, 2019,

S.U. Liu, G.H. Gu, J.Z. Wang, X.J. Cao, “Baxter Target Recognition Algorithm for Cooperative Robot Based on Machine Vision, Electro optic and control”, Vol.26, No.04, pp. 95-99, 2019.

W. Zuo, C.L. Li, L.J. Yu, Z.B. Zhang, R. W. Wang, X.G. Zeng, Y.X. Liu, Y.Y. Xiong, “Shadow–Highlight Feature Matching Automatic Small Crater Recognition Using High-Resolution Digital Orthophoto Map from Chang’ E Missions”, Journal of Geochemistry, Vol.38, No.4, pp. 14-21, 2019. DOI:

J.Y. Ma, T.J. Zhang, G.D. Jing, W.J. Yan, B. Yang, “Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion”, IEEE Access, Vol.8, No.21, pp. 2-18, 2019.

F. Özyurt, “Efficient Deep Feature Selection for Remote Sensing Image Recognition with Fused Deep Learning Architectures”, The Journal of Supercomputing, Vol.76, No.4, pp. 1-19, 2020. DOI:

L.P. Gao, X.L. Zhang, J.P. Gao, S.X. You, “Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition”, IEEE Access, Vol.16, No.28, pp. 65-79, 2020.

Y. Kong, H. Liang, Q. Zhang, Human Behavior Recognition Based on Visual Attention, Computer System Application, Vol.28, No.05, pp. 42-48, 2019.

J. Zeng, D. Du,Automatic Recognition of Weld Bead Trajectory Based on Multi Vision Feature Acquisition and Fusion, Journal of Mechanical Engineering,Vol.55, No.07, pp. 127-136, 2019.

T. Xue, W.H. Liu, Z.Y. Pan, W.M. Wang, “Robot Stable Grasping Based on Visual Perception and Tactile Prior Knowledge Learning”, Robot, Vol.43, No.01, pp. 1-8, 2021.

L. Hong, “Research on Pattern Recognition Method of Machine Vision”, Modern Information Technology, Vol.4, No.11, pp. 83-85, 2020.

S. Ying, Y. Weng, B. Luo, G. Li, B. Tao, D. Jiang, D. Chen, “Gesture Recognition Algorithm Based on Multi-scale Feature Fusion in RGB-D Images”, IET Image Processing, Vol.32, No.21, pp. 123-138, 2020.

W. Wang, C. Tang, X. Wang, Y.H. Luo, Y.L. Hu, J. Li, “Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation”, Computational Intelligence and Neuroscience, Vol.29, No.2, pp. 1-9, 2019. DOI:

C.W. Peng, Z. Yang, “Image Feature Extraction and Object Recognition Based on Vision Neural Mechanism”, International Journal of Pattern Recognition and Artificial Intelligence, Vol.34, No.06, pp. 3340-3342, 2020. DOI:

J. Yao., C. L. Williams., Hussain. F, Kouri. D J. “Generalized Fourier Transform Method for Solving Nonlinear Anomalous Diffusion Equations”, Vol.10, No.12, pp.1039-1047, 2019. DOI:

Y. Zhu, Y. Jiang, “Optimization of Face Recognition Algorithm Based on Deep Learning Multi Feature Fusion Driven by Big Data – ScienceDirect”, Image and Vision Computing, Vol.104, No.21, pp. 65-78, 2020. DOI:

Y. Wu, J. Zhao, N. Yu, “Indoor Surveillance Video Based Feature Recognition for Pedestrian Dead Reckoning”, Expert Systems with Applications, Vol.21, No.21, pp. 55-68, 2021. DOI:

Y. Zhu, W. Min, S. Jiang, “Attribute-Guided Feature Learning for Few-Shot Image Recognition”, IEEE Transactions on Multimedia, Vol.16, No.29, pp. 6-18, 2020.

Shi. Y, “Image recognition of competitive aerobics movements based on embedded system and digital image processing”, Microprocessors and Microsystems, Vol.82, No.Apr., pp.103925.1-103925.6. DOI:

J. Dong, C. Zhang, Y. Wang, “Visual localization Experiment for Robot Intelligent Grasping Task”, Experimental technology and management, Vol.37, No.03, pp. 56-59, 2020.

Y. Wang, Y.R. Li, “Simulation Research on Dynamic Collision Detection Method between Multi Robot Bodies”, Computer Simulation, Vol.37, No.04, pp. 335-339, 2020.

Zhangfang Hu, Lan Wang, Yuan Luo, Yanling Xia, and Hang Xiao, "Speech Emotion Recognition Model Based on Attention CNN Bi-GRU Fusing Visual Information," Engineering Letters, vol. 30, no.2, pp427-434, 2022.

Yi-Cheng Lee, Yu-Chen Wu, and Syh-Shiuh Yeh, "Development of an On-Machine External Thread Measurement System for CNC Lathes Using Eye-in-Hand Machine Vision with Morphology Technology," Engineering Letters, vol. 29, no.3, pp901-912, 2021.




How to Cite

Dong Q. Mobile Robot Vision Image Feature Recognition Method Based on Machine Vision. EAI Endorsed Trans Energy Web [Internet]. 2023 Dec. 20 [cited 2024 Jun. 15];10. Available from:



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