研究目的
To develop a machine vision system for the automated grading of opals, addressing the subjectivity and inconsistency in current opal evaluation practices.
研究成果
The GDA system, utilizing imaging and statistical learning, provides a more consistent and objective method for opal grading compared to manual evaluations. The system's ability to classify and grade opals with high accuracy demonstrates its potential for application in the gemstone industry.
研究不足
The limited number of opals available for experiments may compromise the results. More samples from various classes and grades within each class would help improve experimental results.
1：Experimental Design and Method Selection:
The GDA system mimics human observation of an opal by rotating it under controlled lighting and capturing its images. The images are then analyzed to extract quality features for classification and grading.
2：Sample Selection and Data Sources:
Opals are scanned over the full range of viewing angles with different lighting angles and camera exposures.
3：List of Experimental Equipment and Materials:
The GDA includes a two-axis translucent stage, white LED lighting, a 5-megapixel camera, overhead and angular halogen lights, and a suction system.
4：Experimental Procedures and Operational Workflow:
Calibration is performed daily before image capture. Images are captured at various rotation and tilt angles, and analyzed to extract features.
5：Data Analysis Methods:
Statistical machine learning, specifically support vector machines, is used for opal classification and grading.
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