LBP-and-ScatNet-based combined features for efficient texture classification

Nguyen, Vu-Lam; Vu, Ngoc-Son; Phan, Hai-Hong; Gosselin, Philippe-Henri

doi:10.1007/s11042-017-4824-5

Cited by 5 publications

(2 citation statements)

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“…23 Texture as input to ML algorithms was an active topic in image classification fields such as biomedicine and remote sensing. 24,25 In previous studies, texture descriptors such as Gabor transform, gray level co-occurrence matrix (GLCM), and local binary patterns (LBP) were proposed and used for texture feature extraction. 26 The Gabor transform was proposed earlier to complete the analysis of images from the time-frequency domain.…”

Section: Introductionmentioning

confidence: 99%

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Roughness detection method based on image multi-features

Pan

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.

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