Research on Particle Swarm Image Threshold Segmentation Method Based on Rough Set
DOI:
https://doi.org/10.54097/8ahyyz35Keywords:
Variable Precision Rough Set, Dependency, Particle Swarm Optimization, Image Single-threshold SegmentationAbstract
Image processing is an important way to obtain information and is widely used in important fields such as military, medical and transportation. Image segmentation plays an important role in image processing. In view of the inherent complexity and correlation of the image itself, how to deal with the uncertainty in the image segmentation process is the main work to obtain a more accurate image segmentation result. We propose an image single-threshold segmentation algorithm using the maximum dependency of variable precision rough set (VPRS). The algorithm uses VPRS to represent the image, and uses the maximum dependency and particle swarm optimization to solve the optimal image segmentation threshold, which effectively handles the uncertainty in image segmentation. The experiments show that the single-threshold segmentation algorithm has certain practicability and flexibility, the segmentation effect is better than the maximum average information entropy method, and the segmentation efficiency is significantly higher than the ordinary iterative method.
Downloads
References
[1] Zhu Liangkuan, Liu Liang, Dong Xu, et al. Forest canopy image segmentation based on improved 3D Otsu method[J]. Computer Engineering. 2019, Vol. 45(No. 01), p. 253-258+263.
[2] Lei Xiangxiao, Ouyang Honglin, Xiao Leyi, et al. Research on image segmentation based on equivalent 3-D entropy and whale optimization algorithm[J]. Computer Engineering. 2019, Vol. 45(No. 04), p. 217-222.
[3] Nabanita Mahata, Jamuna Kanta Sing. A novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies for noisy 3D brain MR image segmentation[J]. Applied Soft Computing Journal. 2020, Vol. 90.
[4] Xiaofeng Yue, Hongbo Zhang. Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation[J]. Applied Soft Computing Journal. 2020, Vol. 90.
[5] Zhang Wenxiu, Wu Weizhi, Liang Jiye. Rough Set Theory and Method [M]. Science Press, 2001.
[6] Sun Quansen, Ji Zexuan. Fuzzy Clustering for Brain MR Image Segmentation [J]. Journal of Data Acquisition and Processing. 2016, Vol. 31(No. 1), p. 28-42.
[7] Li Tingting, Jiang Zhaohui, Rao Yuan, et al. Image segmentation based on gene expression programming and spatial fuzzy clustering [J]. Journal of Image and Graphics. 2017, Vol. 22(No. 5), p. 575-583.
[8] Zhang C, Pan X , Zhang S , et al. A rough set decision tree based MLP-CNN for very high resolution remotely sensed image classification[J]. Isprs International Archives of the Photogrammetry Remote Sensing & Spatial Information Sciences. 2017, p. 1451-1454.
[9] Zhang Changsheng, Feng Guang, Liu Ziyu, et al. Research on improved two-dimension Otsu algorithm for SF6 pressure dial image segmentation [J]. Transducer and Microsystem Technologies. 2017, Vol. 36(No. 7), p. 8-11.
[10] Zhang Yingchun, Guo He. Level set Image segmentation based on rough set and new energy formula [J]. Acta Automatica Sinica. 2015, Vol. 41(No. 11), p. 1913-1925.
[11] Zhang Yongmei, Bad Kay, Xing Kuo. A method of fuzzy threshold for adaptive image segmentation [J]. Computer Measurement & Control. 2016, Vol. 24(No. 4), p. 126-128.
[12] Xu Luping. Digital Image Processing [M]. Science Press, 2007.
[13] Phophalia A, Mitra S K. 3D MR image denoising using rough set and kernel PCA method[J]. Magnetic Resonance Imaging. 2017, Vol. 36, p. 135.
[14] Ji Z, Huang Y, Sun Q, et al. A rough set bounded spatially constrained asymmetric gaussian mixture model for image segmentation[J]. PLoS ONE. 2017, Vol. 12(No. 1), p. 697-708.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Frontiers in Computing and Intelligent Systems
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
