An Optimal Fuzzy System for Edge Detection in Color Images Using Bacterial Foraging Algorithm

Verma, Om Prakash; Parihar, Anil Singh

doi:10.1109/tfuzz.2016.2551289

Cited by 79 publications

(25 citation statements)

References 33 publications

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“…Here, the BFO algorithm was used to optimize the fuzzy membership function and the parameters of the enhanced fuzzy operator. (14) However, although these studies have improved the optimization ability of the BFO algorithm in practical applications, most of the recent studies had problems with a large number of parameters that were not easy to control. (15,16) In particular, fixed values were used for some important parameters, limiting the performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Robust Bacterial Foraging Algorithms Based on Few-excellent-individuals Guidance Strategy

Gao¹,

Yu²,

Dai³

et al. 2020

Sensors and Materials

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Keywords: bacterial foraging optimization, 80/20 rule, constriction factor PSO, gradient migration probability, robustness In recent years, the novel bacterial foraging optimization has been widely applied. However, in past studies, the process of bacterial foraging lacked guidance and the structure of the algorithm was inadequate, which resulted in a low convergence speed and a large number of parameters in the algorithm, thus reducing its search accuracy and speed. Additionally, researchers only improved the algorithm for complex situations, for which a comprehensive evaluation of its robustness could not be made. Here, to resolve these issues, two improved algorithms are proposed and compared comprehensively. Our algorithms are suitable for modeling the foraging process of organisms in nature: a small number of individuals with rich resources can attract other individuals to forage locally. First, we propose a decreasing composite function and gradient migration behavior and introduce the 80/20 rule. A few excellent individuals guide the population to migrate to the optimal solution and increase the convergence speed. Second, we introduce the renewal speed of particles and propose another composite function, and the biological characteristics of Escherichia coli are also introduced to achieve the screening of excellent individuals. Finally, we show the results of numerous experiments and comprehensively evaluate the applicability of the proposed organisms.

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Section: Introductionmentioning

confidence: 99%

Robust Bacterial Foraging Algorithms Based on Few-excellent-individuals Guidance Strategy

Gao¹,

Yu²,

Dai³

et al. 2020

Sensors and Materials

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show abstract

“…Commonly used SAR image edge detection operators are carried out in a single window. However, due to the serious interference of speckle noise and other objects in the imaging process of symmetrical difference nuclear SAR images, the detection results under a single window cannot meet the actual needs of a high integrity and low false detection rate at the same time [7][8][9]. Therefore, in order to improve the integrity of edge detection and the effect of noise suppression, it is necessary to detect and fuse the detection results of a symmetrical difference SAR image.…”

Section: Introductionmentioning

confidence: 99%

Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model

Zhang

Liu

et al. 2019

Symmetry

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The symmetrical difference kernel SAR image edge detection algorithm based on the Canny operator can usually achieve effective edge detection of a single view image. When detecting a multi-view SAR image edge, it has the disadvantage of a low detection accuracy. An edge detection algorithm for a symmetric difference nuclear SAR image based on the GAN network model is proposed. Multi-view data of a symmetric difference nuclear SAR image are generated by the GAN network model. According to the results of multi-view data generation, an edge detection model for an arbitrary direction symmetric difference nuclear SAR image is constructed. A non-edge is eliminated by edge post-processing. The Hough transform is used to calculate the edge direction to realize the accurate detection of the edge of the SAR image. The experimental results show that the average classification accuracy of the proposed algorithm is 93.8%, 96.85% of the detection edges coincide with the correct edges, and 97.08% of the detection edges fall into the buffer of three pixel widths, whichshows that the proposed algorithm has a high accuracy of edge detection for kernel SAR images.

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“…However, factor spaces were introduced based on Zadeh fuzzy sets, whereas the fuzzy decision and control of (-∞, 0) and (1, +∞) are also important. Professor Kaiquan Shi proposed the concept of both-branch fuzzy sets (Verma & Parihar 2017) in 1997, and the proposal of the -Fuzzy set in 2008 by Professor Hongxing Li accommodated for the shortcomings of Zadeh fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

R-Fuzzy Sets in Factor Space

Liu¹,

Mi²,

Choy³

2019

JSM

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Interval-valued fuzzy sets, both-branch fuzzy sets and-Fuzzy sets are three extended forms of ordinary fuzzy sets. Compared to traditional fuzzy sets, interval-valued fuzzy sets have a stronger ability to express uncertainty because they provide more choices for the descriptions of attributes. Using both-branch fuzzy sets has solved some problems in engineering decision and engineering control. For-Fuzzy sets, a new fuzzy system is constructed, with both-branch fuzzy sets as a special case. The resulting theorems improve the flexibility of uncertainty system models and expand the range of fuzzy system applications as well.

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An Optimal Fuzzy System for Edge Detection in Color Images Using Bacterial Foraging Algorithm

Cited by 79 publications

References 33 publications

Robust Bacterial Foraging Algorithms Based on Few-excellent-individuals Guidance Strategy

Robust Bacterial Foraging Algorithms Based on Few-excellent-individuals Guidance Strategy

Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model

R-Fuzzy Sets in Factor Space

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