Froth image clustering with feature semi-supervision through selection and label information

Cao, Wenyan; Ran-feng, Wang; Fan, Minguang; Fu, Xiang; Wang, Yulong; Guo, Zhongtian; Fan, Fubo

doi:10.1007/s13042-021-01333-5

Int. J. Mach. Learn. & Cyber.

2021

DOI: 10.1007/s13042-021-01333-5

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Froth image clustering with feature semi-supervision through selection and label information

Wenyan Cao

Wang Ran-feng

Minguang Fan

et al.

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Cited by 6 publications

References 39 publications

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A low-light image enhancement method for personnel safety monitoring in underground coal mines

Yang,

Wang,

et al. 2024

Complex Intell. Syst.

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Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detection accuracy. To alleviate this problem, a low illuminance image enhancement method proposed for personnel safety monitoring in underground coal mines. Specifically, the local enhancement module maps low illumination to normal illumination at pixel level preserving image details as much as possible. The transformer-based global adjustment module is applied to the locally enhanced images to avoid over-enhancement of bright areas and under-illumination of dark areas, and to prevent possible color deviations in the enhancement process. In addition, a feature similarity loss is proposed to constrain the similarity of target features to avoid the possible detrimental effect of enhancement on detection. Experimental results show that the proposed method improves the detection accuracy by 7.1% on the coal mine underground personal dataset, obtaining the highest accuracy compared to several other methods. The proposed method effectively improves the visualization and detection performance of low-light images, which contributes to the personnel safety monitoring in underground coal mines.

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A low-light image enhancement method for personnel safety monitoring in underground coal mines

Yang,

Wang,

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

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A density map regression method and its application in the coal flotation froth image analysis

Fan

Wang

et al. 2022

Measurement

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Deep correlation and precise prediction between static features of froth images and clean coal ash content in coal flotation: An investigation based on deep learning and maximum likelihood estimation

Lu,

Liu,

2024

Measurement

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Froth image clustering with feature semi-supervision through selection and label information

Cited by 6 publications

References 39 publications

A low-light image enhancement method for personnel safety monitoring in underground coal mines

A low-light image enhancement method for personnel safety monitoring in underground coal mines

A density map regression method and its application in the coal flotation froth image analysis

Deep correlation and precise prediction between static features of froth images and clean coal ash content in coal flotation: An investigation based on deep learning and maximum likelihood estimation

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