Affiliation:
1. School of Artificial Intelligence, Chongqing Three Gorges Vocational College , Wanzhou, Chongqing , 404000 , China
Abstract
Abstract
A method based on the contour coefficient and image processing technology is proposed to better identify the visual elements. This article takes the contour of the image as the recognition feature, summarizes the methods of target contour feature extraction, contour shape representation, and similarity representation, and studies the processing methods of contour edge preserving and denoising, contour feature simplification and description methods, and contour matching methods. This problem can usually be solved by filling out a form. Generally, a simple iterative equation is given to express the direct relationship between the current table and the calculated table values. The dynamic programming algorithm of inner distance shape context, multi-scale convexity convexity, and triangle area representation finds the best sequence correspondence.
Subject
Behavioral Neuroscience,Artificial Intelligence,Cognitive Neuroscience,Developmental Neuroscience,Human-Computer Interaction
Reference27 articles.
1. W. Chen, B. Chen, X. Peng, J. Liu, and H. Liu, “Tensor RNN with Bayesian nonparametric mixture for radar HRRP modeling and target recognition,” IEEE Trans. Signal. Process, vol. 69, pp. 1995–2009, 2021.
2. C. Mao, L. Huang, Y. Xiao, F. He, and Y. Liu, “Target recognition of sar image based on CN-GAN and CNN in complex environment,” IEEE Access, vol. 9, pp. 39608–39617, 2021.
3. H. Wang, “Multi-sensor fusion module for perceptual target recognition for intelligent machine learning visual feature extraction,” IEEE Sens. J., vol. 22, pp. 17431–17438, 2021.
4. G. Xiong, Y. Xi, D. Chen, and W. Yu, “Dual-polarization SAR ship target recognition based on mini hourglass region extraction and dual-channel efficient fusion network,” IEEE Access, vol. 9, pp. 29078–29089, 2021.
5. Z. Li, Q. Zhang, T. Long, and B. Zhao, “Ship target detection and recognition method on sea surface based on multi-level hybrid network,” J. Beijing Inst. Technol., vol. 30, no. zk, pp. 1–10, 2021.