A method for textural feature extraction based on Gabor wavelet and improved local binary pattern (LBP) is proposed to classify and recognize the surface roughness of a work piece through its image and improve accuracy. Given that the LBP operator ignores the magnitude differences between neighbors

the magnitude-considered LBP (M_LBP) operator is proposed. The magnitude of the gray level differences between neighbors is defined as . The gray mean of the image is used as threshold for the binarization of . The binarization result is appended to the top digit of the LBP in this neighbor

which is obtained by dividing the LBP according to the value. Before the recognition of the surface roughness of the work piece

the surface image is obtained with a stereomicroscope and then preprocessed. A self-similar Gabor wavelet filter bank is acquired by changing the scale and orientation parameters. The filter bank is used for surface image filtering. The multi-scale and multi-resolution Gabor texture features of the image are acquired. Afterward

the magnitude of the Gabor texture features is calculated

and the Gabor magnitude maps (GMMs) are extracted. The proposed M_LBP operator is then applied in the GMMs to extract M_LBP feature maps. Based on these M_LBP feature maps

the texture feature vector for each surface image can be constructed. After extracting these texture feature vectors

the k-nearest neighbor (KNN) algorithm is used in roughness recognition. The feature vectors of both the training samples and the samples to be recognized are extracted. Then

the

nearest neighbors of the samples to be recognized are selected from the training samples. The roughness class of the sample to be recognized can be acquired according to the roughness classes of these nearest neighbors. Different values are selected for (

) in the M_LBP operator and for in the KNN algorithm. The comparative experiment shows that the recognition accuracy is highest when (

) is set to (8

8) and is set to 4. We use the LBP

Gabor combined with LBP

and Gabor combined with M_LBP to extract texture feature vectors. Through these vectors

we compare the time consumption (0.2886

0.9546

and 1.1562 s) and the recognition accuracy (74%

82%

and 98%). The experimental results demonstrate that the proposed method can recognize the surface roughness of the work piece with 98% accuracy and a difference of 0.2 μm

which is better than that of the other two algorithms. The proposed M_LBP operator can refine LBP information

and the Gabor wavelet combined with M_LBP overcomes the limitations of LBP

which includes single scale

single orientation

and disregard for magnitude. Hence

the method can be applied in roughness recognition with high precision.