Binary particle swarm optimization based edge detection under weighted image sharpening filter

Verma, Ankush; Dhanda, Namrata; Yadav, Vibhash

doi:10.1007/s41870-022-01127-0

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…However, marked regions 1 and 6 still reveal missing edges. Results for Verma et al [34] whose method is based on PSO and sharpening, are displayed in Figure 8(h), and it is evident that its performance is good. Although the majority of edges have been located, designated regions 3 still have some missing edges.…”

Section: Resultsmentioning

confidence: 99%

“…Raheja and Kumar [36] proposed Fuzzy + L0 guided filtering method, and the Fscore improved to 0.84 with a FoM of 0.5. Verma et al [34] proposed PSO+ sharpening, and the F-Score improved to 0.851. Ranjan et al [43] proposed Fuzzy+ Weighted Guided Filtering with an impressive FoM of 0.58.…”

Section: Resultsmentioning

confidence: 99%

“…This method produces better results as compared to the traditional Sobel operator, but is still unable to detect some of the genuine edges. Verma et al [34] considered image sharpening along with PSO for edge detection; however, the main limitation is noise in edge detection.…”

Section: Soft Computing Based Image Edge Detectionmentioning

confidence: 99%

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An Expert System Based on Type-1 Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

Ranjan

Avasthi

2023

IJE

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In computer vision, contour/edge detection is a crucial phenomenon. Edge detection is an important step in contour detection, which is helpful in the identification of important data. The accuracy of the edge detection process is heavily dependent on edge localization and orientation. In recent years, due to their versatility, soft computing approaches have been considered effective edge detection strategies. Broadly, edge detection accuracy is deeply affected by weak and dull edges. In recent works, edge detection based on fuzzy logic (FL) was proposed, and image edges were improved using guided filtering. However, guided image filtering (GIF) does not take into account the local features of an image. To include local features of an image for edge detection, an improved version, i.e., an offset enable sharpening-guided filter is used in this paper, and FL is used for edge detection. The figure of merit (FoM) and F-score are used to evaluate the method's accuracy. Using visual representations and performance metrics, the results are compared with those from cutting-edge techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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An Expert System Based on Type-1 Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

Ranjan

Avasthi

2023

IJE

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Modified Bird Swarm Algorithm for blind image steganalysis

Chhikara

Sharma

Chandra

et al. 2023

Int. j. inf. tecnol.

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CLPB: chaotic learner performance based behaviour

Franci,

Rashid

2024

Int. j. inf. tecnol.

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Learner Performance-based Behavior (LPB) is a recent metaheuristic algorithm that is inspired by the process of accepting students who have recently graduated from high schools into various departments at university according to the student's GPA (Grade Point Average). Two versions of the algorithm are available; single and multi-objective versions. The exploitation phase, convergence speed, and processing time (PT) of the LPB are not according to the required level.This paper aims to improve the performance of a single objective LPB through modi cation which helps in reducing its PT, getting closer to the global optima, and bypassing the local optima with the best convergence speed. For that, ten chaos theory maps are used within LPB to propose Chaotic LPB (CLPB) to consider which of the ten maps works well in case of performance. Another modi cation that has been made to LPB is that the best individuals of a sub-population are forced into the interior crossover to improve the quality of solutions. CLPB is evaluated against multiple well-known test functions such as classical (TF1_TF19) and (CEC_C06 2019). Additionally, the results have been compared to the standard LPB and several wellknown metaheuristic algorithms such as DA, GA, and PSO. Finally, the numerical results show that CLPB has been improved with chaotic maps. Furthermore, it is veri ed that CLPB has a great ability in dealing with large optimization problems compared to LPB, GA, DA, and PSO. Overall, Gauss and Tent maps both have a great impact on improving CLPB.

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Binary particle swarm optimization based edge detection under weighted image sharpening filter

Cited by 5 publications

References 32 publications

An Expert System Based on Type-1 Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

An Expert System Based on Type-1 Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

Modified Bird Swarm Algorithm for blind image steganalysis

CLPB: chaotic learner performance based behaviour

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