Threshold image target segmentation technology based on intelligent algorithms

Abstract

This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.

Multithreshold Microbial Image Segmentation Using Improved Deep Reinforcement Learning

“…According to the data in Table 1, different algorithms are used to segment three data sets. e number of thresholds determined by the algorithms in Li et al [6], Li et al [7], and Cai et al [8] is less than other algorithms, which seriously affects the image segmentation performance of the algorithm. is is the reason why its NAE and MSE indexes are higher than other algorithms, and the operation efficiency of the above-given algorithms is low.…”

“…e quantum-based image segmentation algorithm in Li et al [6], the parallel Lévy grey wolf optimized segmentation algorithm in Li et al [7], the segmentation algorithm based on the Intelligent Algorithm in Cai et al [8], the Lévy flight improved multithreshold segmentation algorithm in Jia et al [9], and the target recognition algorithm of deep reinforcement learning in Sun et al [10] are used as comparison algorithms.…”

“…However, this method still results in a serious loss of image detail information. Cai et al [8] proposed the use of an improved particle swarm optimization algorithm to achieve segmentation processing of thresholded images, and the method obtained a small number of image thresholds, which has limitations in applying it to the segmentation of a variety of microbial images. Jia et al [9] proposed to improve the multiverse optimization algorithm using the Leyland flight mechanism to improve the accuracy of image segmentation by accurately determining the number of image thresholds.…”

Image segmentation of rail surface defects based on fractional order particle swarm optimization 2D-Otsu algorithm

“…Japanese scholar Otsu proposed in 1979 that Otsu technology can segment some images well [1] . In 2020, Yanxia C et al improved the Particle Swarm optimization algorithm (PSO) and introduced the optimal threshold calculation model [2] . The results show that the improved PSO has good performance, fast convergence speed and high fitness value.…”