Quantitative grading of opals is a challenging task even for skilled opal assessors. Current opal evaluation practices are highly subjective due to the complexities of opal assessment and the limitations of human visual observation. In this paper, we present a novel machine vision system for the automated grading of opals—the gemological digital analyzer (GDA). The grading is based on statistical machine learning with multiple characteristics extracted from opal images. The assessment workflow includes calibration, opal image capture, image analysis, and opal classification and grading. Experimental results show that the GDA-based grading is more consistent and objective compared with the manual evaluations conducted by the skilled opal assessors.
['Dadong Wang', 'Leanne Bischof', 'Ryan Lagerstrom', 'Volker Hilsenstein', 'Angus Nelson Hornabrook', 'Graham Alfred Hornabrook']
Automated Opal Grading by Imaging and Statistical Learning