Image segmentation using K-means clustering, Gabor filter and moving mesh method

Shi, Hongcan; Lee, Wan-Lung

doi:10.1080/13682199.2022.2161159

Cited by 2 publications

(1 citation statement)

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“…. , V K } [31]. Firstly, the algorithm randomly determines K points as the initial clustering center, calculates the similarity of each sample point to the clustering center one by one, and classifies the sample points into the most similar class according to the size of the similarity.…”

Section: K-means Clustering Complex Image Background Segmentationmentioning

confidence: 99%

Multiple damage segmentation and extraction of wind turbine blades surface under complex background based on SKRT approach

Wang,

Zheng,

Lang

et al. 2023

Meas. Sci. Technol.

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Machine vision detection technology has been widely used in detecting wind turbine blade surface damage, but the complex background often has a significant impact on blade damage detection. Aiming at the complex background blade images caused by various natural background features, complex blade surface features, low contrast features, non-uniform illumination, weather conditions, and other factors, the work proposes an SKRT segmentation method based on K-means clustering and adaptive threshold fusion to segment and extract multiple damage features on the surface of wind turbine blades with complex backgrounds. Firstly, an single-scale retinex(SSR) algorithm is adopted to enhance the contrast between surface damage and image background in the research. Then the image is roughly segmented by K-means clustering to remove most of the background features, and the background mask is replaced with the peak of the remaining feature image pixels to solve the problem of wrong segmentation. Finally, making use of the adaptive threshold local segmentation method to accurately segment the blade surface damage. The experimental results show that the proposed SKRT segmentation method can significantly improve the segmentation accuracy of wind turbine blade surface damage with complex background, and the accuracy, intersection over union, and F-measure value are increased by 37.20%, 35.71%, and 28.69%, respectively. The method performs better robustness to multiple damage feature segmentation.

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Section: K-means Clustering Complex Image Background Segmentationmentioning

confidence: 99%