HSV空间下改进的直觉模糊C均值聚类猪肉图像检测

doi:10.3969/j.issn.1000-1158.2021.08.02

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Abstract There are many methods to classify meat by using computer vision technology. For the problems of poor segmentation effect and poor noise adaptability of traditional maximum variance (OTSU) method and the problems of large size, unportability and high cost of detection instruments in most nondestructive testing methods such as nuclear magnetic resonance (NMR) and hyperspectral imaging, HSV color space combined with clustering method is proposed to cluster and segment image pixels. When the pork image is segmented from the natural lighting environment, the proposed method is improved by an average of 1.46% compared with the traditional clustering method. When the image with 0.1 and 0.2 salt and pepper noise is segmented, the proposed method has better anti-noise ability than the traditional method. The average error rate of the traditional segmentation method has increased by 16.15% and 38.28% respectively. The average error rate of the proposed method has only increased by 1.57% and 1.49%. The accuracy of image segmentation and noise robustness improve the detection accuracy of the target region, reduce the information loss in the image preprocessing stage, and improve the quality of the meat classification method.

Key words： metrology livestock meat classification image segmentation C-means clustering intuitionistic fuzzy entropy HSV detection of pork

Received: 19 January 2020 Published: 20 August 2021

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TB96

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	Articles by authors
	WANG Di
	DONG Su-fen
	CHENG Fang
	ZHAO Yan
	LI Jin

Cite this article:

WANG Di,DONG Su-fen,CHENG Fang, et al. Improved Intuitionistic Fuzzy C-means Clustering Pork Image Detection in HSV Space[J]. Acta Metrologica Sinica, 2021, 42(8): 986-992.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.08.02 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I8/986

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