원문정보
초록
영어
Identifying the specific machining processes used to produce specific workpiece surfaces is very useful in materials inspection. Machine vision can be used to semi- or fully automate this identification process by firstly extracting features from captured workpiece images, followed by analysis using machine learning algorithms. This enables inspection to be performed more reliably with minimal human intervention. In this paper, three visual texture features are investigated to classify machined workpiece surfaces into the six machining process classes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. These are the multi-directional Gabor filtered images, intensity histogram and edge features statistics. Support Vector Machines (SVM) applying different kernel functions are investigated for best classifier performance. Results indicate that the Gabor-based SVM-linear kernel provides superior performance.
목차
1. Introduction
2. Features Extraction
2.1. Gabor Filters
2.2. Intensity Histogram
2.3. Edge Features Statistics
3. Feature Dimensionality Reduction
3.1. Gabor Filtered Image Features
3.2. Intensity Histogram and Edge Feature Statistics
3.3. Principal Components Analysis
4. Supervised Learning: The Support Vector Machine
5. The Dataset
6. Workflow and Experimental Setup
7. Results and Discussion
8. Conclusion
Acknowledgments
References