Two-Dimensional Maximum Entropy Infrared Image Fast Segmentation Based on Chicken Swarm Optimization

Niu, Ben; Mu, Xiaodong; Yi, Zhaoxiang; Hu, Pei

doi:10.1109/ihmsc.2018.00042

Cited by 3 publications

(2 citation statements)

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“…According to the principle of acoustic reflection imaging, it has the characteristics of high gray value of the target. Therefore, the maximum two-dimensional entropy segmentation principle is used to preserve the A region of the suspected target in the XOY plane of the two-dimensional histogram and remove the background B region.It can not only remove part of the noise enhancement details, but also effectively remove the invalid background to prevent the influence on the subsequent segmentation, making the subsequent segmentation more accurate.In this paper, the maximum value of formula (10) is obtained by using this principle and combining with the chicken flock optimization algorithm [8] , and the critical gray value at the maximum two-dimensional entropy value is obtained. The gray value is set as the segmentation threshold to carry out the segmentation operation of removing background.…”

Section: The Forward Looking Sonar Image Is Processed Without Backgroundmentioning

confidence: 99%

Forward looking sonar image segmentation based on empirical mode decomposition

Tan

Wang

Chen

et al. 2022

J. Phys.: Conf. Ser.

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Due to the low imaging quality and serious noise pollution of forward-looking sonar images, traditional image segmentation methods based on edge information or statistical information are difficult to obtain high precision and robust segmentation results. Therefore, it is very complicated to segment forward-looking sonar images. Based on the analysis of the characteristics of forward-looking sonar, a new image segmentation method for forward-looking sonar is proposed.Firstly, 2d maximum entropy segmentation principle combined with chicken flock optimization algorithm is used to remove the background of the forward-looking sonar image.Then, based on 2d empirical mode decomposition, appropriate eigenmode functions are selected to denoise and enhance the forward-looking sonar image.Finally, the reconstructed forward-looking sonar image is segmented by enhanced fuzzy C-means clustering, and the segmented forward-looking sonar image result is obtained. This method can not only suppress noise interference effectively, but also protect the details of image edge for segmentation.

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Section: The Forward Looking Sonar Image Is Processed Without Backgroundmentioning

confidence: 99%

Forward looking sonar image segmentation based on empirical mode decomposition

Tan

Wang

Chen

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…This algorithm has the characteristic of simple implementation [11][12]. It has been widely applied in wireless sensors [12][13], distribution network [14], image processing [15] and other engineering problems [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

An Improved Chicken Swarm Optimization Algorithm Based on Adaptive Mutation Learning Strategy

Zhou¹,

Zhou²,

Gao³

2022

Journal of Compurters

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<p>To solve the problem that the Chicken swarm optimization (CSO) has low solution accuracy and tends to fall into the local optimum on later stages of iteration, an adaptive mutation learning Chicken swarm optimization (AMLCSO) is proposed in this paper. Firstly, to solve the problem of uneven initial distribution and improve the algorithm’s stability, a good-point set is introduced. Secondly, according to the difference between the current individual position and the optimal individual position, the nonlinear adaptive adjustment of weight is realized and the position update step is dynamically adjusted. This strategy improves the algorithm’s convergence. Thirdly, the learning update strategies of Gaussian mutation and normal distribution are introduced to improve the probability of selection and solving accuracy and avoid falling into the local optimum. Finally, the AMLCSO is compared with other standard algorithms and improved Chicken swarm optimization algorithms on twenty benchmark test functions. The experimental results show the AMLCSO has faster convergence and higher solution accuracy.</p> <p> </p>

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