Invariant Feature Extraction Method Based on Smoothed Local Binary Pattern for Strip Steel Surface Defect

Feature extraction is an important step for strip steel surface defect detection because it is the premise of defect classification and recognition. In order to realize defect feature extraction, effective and separable feature description is needed. There are many features used to describe steel surface defect, such as geometric features,1) one-dimensional histogram features,1) two-dimensional histogram features2,3) and HU invariant moment features.4) Geometric features can describe the shape of defect. One-dimensional histogram features can describe statistic distribution of gray values in region of defect. Two-dimensional histogram features describe the texture in region of defect by using gray-level co-occurrence matrix (GLCM). And HU invariant moment features also belong to geometric features but with rotation, translation and scale invariance. Local binary pattern (LBP)5,6) is an effective description operator of gray changes in local neighborhood. It is invariant in rotation and illumination, which can describe the texture of detection region. Though its theory is simple, it is powerful in texture recognition. In recent years, LBP is used widely in texture analysis,7) face recognition8,9) and image matching.10) However, it still has shortcomings in specific application. So, many scholars have improved it and achieved satisfactory results. Reference8) adopts scalable oval to be local neighborhood. And LBP value is outputted by comparing neighboring pixels on that oval. This method can capture anisotropic information, which is more general than standard LBP. Reference9) introduces new parameters: Invariant Feature Extraction Method Based on Smoothed Local Binary Pattern for Strip Steel Surface Defect