Threshold image target segmentation technology based on intelligent algorithms

Cai, Yanxia; Xu, Yanyi; Zhang, T. R.; Li, D. D.

doi:10.18287/2412-6179-co-630

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Multithreshold Microbial Image Segmentation Using Improved Deep Reinforcement Learning

Zhou¹

2022

Mathematical Problems in Engineering

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Image segmentation technology can effectively extract the foreground target in the image. However, the microbial image is easily disturbed by noise, its greyscale has the characteristics of nonuniform distribution, and several microorganisms with diverse forms exist in the same image, resulting in insufficient accuracy of microbial image segmentation. Therefore, a multithreshold microbial image segmentation algorithm using improved deep reinforcement learning is proposed. The wavelet transform method is used to remove the noise of the microbial image, the threshold number of the microbial image after denoising is determined by calculating the number of peaks of the grey histogram, and the foreground target of the microbial image is enhanced by the mean iterative threshold segmentation method, the preliminary segmentation of the microbial image is realized, the multithreshold microbial image segmentation model based on ResNet-Unet is constructed, and the cavity convolution and dual Q network mechanism are introduced to improve the segmentation model. The preliminary segmented microbial image is input into the improved segmentation model to realize the segmentation of the multithreshold microbial image. The results show that the proposed algorithm can effectively remove the noise of microbial images. With the increase in the number of thresholds, the peak signal-to-noise ratio, structural similarity, and feature similarity show an upward trend, and the loss rate of the model is less than 0.05%. The minimum running time of the algorithm is 3.804 s. It can effectively and quickly segment multithreshold microbial images and has important application value in the field of microbial recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multithreshold Microbial Image Segmentation Using Improved Deep Reinforcement Learning

Zhou¹

2022

Mathematical Problems in Engineering

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“…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.…”

Section: Introductionmentioning

confidence: 97%

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

Geng,

Sheng,

Sun

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Under the influence of high density operation and natural environment, the rail surface will appear abrasion damage, which will affect the safety and comfort of the train. Rail surface defect detection is an important part to ensure the safe and efficient operation of railway system. In order to distinguish whether there are defects on the rail surface, a method of rail surface defect image segmentation based on FPSO 2D-Otsu algorithm is proposed. The rail image is denoised and enhanced by adaptive fractional calculus, and then the rail image is segmented by FPSO 2D-Otsu algorithm. In order to verify the accuracy of the algorithm, the proposed algorithm is compared with PSO 2D-Otsu image segmentation algorithm. The experimental results show that the accuracy of FPSO 2D-Otsu algorithm in rail image segmentation is improved from 48.76% to 83.59% compared with PSO 2D-Otsu algorithm.

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