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How accurately do different computer-based texture characterization methods predict material surface coarseness? A
Summary
Computer-based texture analysis accurately predicts material coarseness. Gray level co-occurrence matrix (GLCM) and histogram skewness (SK) excel at high coarseness, guiding industrial inspections.
Area of Science:
- Materials Science
- Computer Vision
- Industrial Inspection
Background:
- Industrialization drives demand for automated material inspection.
- Computer-based texture analysis offers potential for objective surface characterization.
- Human visual inspection of surface coarseness can be subjective and inconsistent.
Purpose of the Study:
- To evaluate four computer-based texture characterization methods for predicting surface coarseness.
- To compare the performance of Gray Level Co-occurrence Matrix (GLCM), Distance-Dependent Edge Frequency (DDEF), Fractal Dimension (FD), and Histogram Skewness (SK).
- To assess the impact of image resolution on method performance and provide guidance for industrial applications.
Main Methods:
- Utilized a novel collection of 20 real sandpaper samples across high, medium, and low coarseness levels.
- Applied four texture characterization methods: GLCM, DDEF, FD, and SK.
- Analyzed predictions of both visually perceived and actual surface coarseness.
Main Results:
- At high coarseness, GLCM and SK provided the most precise predictions of actual surface coarseness.
- All methods performed similarly effectively in predicting visual coarseness.
- Perfect correlations between GLCM, FD, and SK were found at medium coarseness levels for both visual and actual coarseness.
- SK and DDEF showed acceptable predictions for visual and actual coarseness, respectively, at low coarseness levels.
- Image resolution significantly impacted the performance of all tested computer-based methods.
Conclusions:
- Computer-based texture analysis methods can serve as effective substitutes for human observers in online material inspections.
- The choice of method (GLCM, DDEF, FD, SK) depends on the specific coarseness level and whether visual or actual coarseness is being predicted.
- Image resolution is a critical factor to consider when implementing these automated inspection systems.
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