A novel method for automated defect detection of pearls based on machine vision is proposed. Firstly, a dome-shaped
light source with diffused light illumination was designed to improve image quality and reduce light-spot size. And a
novel quasi-synchronous multi-images grabbing scheme from different views is then designed based on pearl'
free-falling motion. Then a nonlinear filter based on space geometry is given to enhance defect contrasts following by a
region-grow method for extracting all suspicious defects, including highlight-halation regions. Furthermore, the
highlight-halation regions were removed using morphological method based on the spatial distributive model of the
highlight-halation. At last, shape and texture features of defect regions are extracted and SVM method was used for
defect grading. Experiments show that the acquired images included the complete information of pearl surfaces and the
system correctness was over 93.3% .
The pearl color and color uniformity are important to its price. The paper presented a new real-time method to measure and grade the pearl color based on optoelectronic techniques. The method uses HSI (Hue, Saturation and Intensity) color model, diffuse reflection illumination, CCD (Charge Couple Device) camera with fine color reproduction and digital image processing technology to realize the parameters of color matrix of pearl color. These parameters, including mean values (h and s), variances (σ<sub>h</sub> and σ<sub>s</sub>) and absolute values of third moments (|t<sub>h</sub>| and |t<sub>s</sub>|) of H and S, were acquired. Then the industrial control computer acquires the grading signals. The grading devices graded the pearls. The method could measure and grade the pearl on real-time and meet the requirement of grading.